SnowPro Core Certification Dumps & Practice Questions for COF-C02 Exam

Get exam-ready for the Snowflake SnowPro Core Certification (COF-C02) with this ultimate set of updated dumps and practice questions. Designed for data professionals, architects, and engineers, these SnowPro Core dumps help you master key topics including Snowflake architecture, data loading, performance optimization, SQL, and security. With realistic SnowPro Core practice exams and sample questions aligned to the COF-C02 blueprint, you’ll gain the confidence and skills needed to pass the exam on your first attempt. Ideal for both beginners and experienced cloud practitioners, this comprehensive preparation material ensures you're fully equipped for Snowflake certification success in 2026 and beyond.

SnowPro Core Certification Dumps & Practice Questions List :

1. A global organization using the Snowflake Data Cloud wants to optimize their costs and performance across multiple cloud providers. Which of the Snowflake's cloud partner categories should primarily be engaged to assist in managing, monitoring, and optimizing the company's Snowflake expenditure and usage across these cloud environments?

1. Data Integration Partners

2. Migration Service Partners

3. Data Governance Partners

4. Data Management Partners

2. In a Snowflake environment, you've noticed that some of your queries that used to run in seconds are now taking minutes. You suspect it might be related to the compute resources. Which of the following could be a possible reason for this degradation in performance?

1. The Snowflake virtual warehouse used is of size 'X-Small'.

2. The storage costs have been increasing steadily.

3. Automatic clustering of the table has been enabled.

4. Multi-cluster warehouses have been disabled.

3. Your team has complained about slower query performance for the last week. Upon investigation, you found that multiple large ETL jobs were running during business hours, using the same virtual warehouse. To ensure consistent performance for both ad-hoc queries and ETL jobs, which of the following strategies would you adopt?

1. Create two separate virtual warehouses: one for ETL tasks and one for ad-hoc queries.

2. Increase the size of the existing virtual warehouse.

3. Implement resource monitors to halt any long-running ETL job.

4. Set the multi-cluster warehouse for the ETL jobs to spin up additional clusters during high demand.

4. Your organization has regulatory requirements to retain data for seven years. You need to set up a Snowflake environment that provides efficient data retrieval but also meets compliance. How can you leverage Snowflake's Storage Layer to meet this requirement while optimizing costs?

1. Regularly export older data to external storage and reimport when needed.

2. Set the Time Travel retention period to seven years for all tables.

3. Implement a strategy to archive older data into separate tables and databases.

4. Utilize Snowflake's Fail-safe feature to retain data for seven years.

5. You have been given a set of raw data files that contain customer survey responses. These files are available in Parquet and Avro formats. The data includes various data types such as integers, strings, dates, and arrays. Which format should you prioritize when loading this data into Snowflake, considering the supported file formats and data types?

1. Load the data in Parquet format because Snowflake provides better performance and optimization for querying Parquet files.

2. Convert the data to CSV format as it is the most widely supported format in Snowflake and can handle various data types.

3. Load the data in both Parquet and Avro formats simultaneously to ensure redundancy and data availability.

4. Prioritize loading the data in Avro format since it is a more efficient and space-saving format compared to Parquet.

6. XYZ Corporation operates in a heavily regulated industry and needs to ensure that only authorized personnel can access and modify certain sensitive datasets within their Snowflake environment. Additionally, they want to enforce strict monitoring and auditing of all data access and modifications. Which Snowflake data governance capability would be most suitable for addressing this complex scenario?

1. Snowflake Data Masking

2. Row Access Policies

3. Resource Monitors

4. Query Tagging

7. You are working on a project that involves processing external files stored in a Snowflake stage. The files contain customer reviews in JSON format, including information about the product, the reviewer's name, and the review content. Your goal is to extract this data and load it into a structured table in Snowflake. Which of the following Snowflake SQL file functions would be suitable for this task?

1. IMPORT DATA

2. COPY INTO

3. GET_METADATA

4. PARSE_JSON

8. Your company is implementing data sharing with a third-party analytics service provider. To minimize data transfer costs and ensure that the third party can only query the most recent data, which of the following approaches is the most effective for setting up the share in Snowflake?

1. Share the data using Secure Data Sharing by creating a share on the latest view of the dataset.

2. Share an external table that the third party can refresh at their convenience.

3. Create a full database share and update its contents daily.

4. Set up a continuous data pipe to push the latest data to an external location, which the third party can then ingest.

9. Your company recently experienced unexpected billing spikes in Snowflake. Upon investigation, you found that it was related to compute resources. The pattern shows high concurrency during end-of-month reporting. How can you optimize costs without compromising performance during these high-demand periods?

1. Deploy a multi-cluster virtual warehouse and set both minimum and maximum clusters to handle concurrency.

2. Reduce the size of the virtual warehouse to "Small" during the end-of-month period.

3. Move the most accessed tables to materialized views during the reporting period.

4. Use Snowflake's Data Exchange to offload some of the querying tasks.

10. Your organization is running complex analytical queries on Snowflake and has set up multiple warehouses to manage the load. You notice one of your important queries is taking longer than expected. Which of the following could be a reason for the slowdown?

1. The data is stored in multiple databases rather than multiple schemas.

2. The data is stored in a VARIANT column.

3. You're using Snowsight instead of the classic UI.

4. The warehouse size is not large enough.

5. The caching mechanism of Snowflake is disabled.

11. You are tasked with unloading data from Snowflake into external files for further processing by downstream systems. The data consists of sensitive customer information, and security is a top concern. Additionally, the downstream systems require the data in a specific format. What are the best practices and considerations for unloading data in this scenario?

1. Use the CREATE EXTERNAL TABLE command with a secure stage, specifying the desired format. Apply column-level security and encryption before unloading data.

2. Use the UNLOAD command with the OVERWRITE = TRUE option and specify the desired format. Grant access to the target location for relevant roles.

3. Utilize the EXPORT statement with the REQUIRE PRIVATE LINK option to ensure secure data transfer. Convert the data to the required format using an external ETL tool.

4. Use the COPY INTO command with the ENCRYPTED option, specifying the target format. Ensure the appropriate roles and permissions are set for the target location.

12. You've noticed that certain complex queries are taking longer than expected to execute. Assuming all other factors are constant, which action related to Snowflake's Compute Layer would most likely improve the execution time of these queries?

1. Increasing the size of your virtual warehouse.

2. Enabling automatic clustering on the target table.

3. Decreasing the data retention time for Time Travel.

4. Creating a separate schema for the queried tables.

13. When unloading data from Snowflake to a single file, which of the following considerations should be taken into account to optimize performance and ensure data accuracy in a complex data transformation scenario?

1. Perform data type conversions during unloading to match the target format.

2. Convert all data to text format for uniformity in the output file.

3. Apply row-level filtering during unloading to exclude irrelevant data.

4. Use a single, large file for simplicity and ease of management.

14. You have implemented a microservices architecture for a data-intensive application. These services primarily interface with Snowflake using the JDBC driver. However, certain services experience intermittent connection timeouts during peak hours. What could be a plausible reason for this issue?

1. The services are making DDL statements which are inherently slow.

2. The Snowflake account's resource monitor has paused the assigned warehouse due to excessive credit consumption.

3. The services are connecting to Snowflake from multiple regions leading to networking lag.

4. The Snowflake account has run out of storage space.

15. You are working with a large retail company that collects real-time customer data from various sources. The company wants to analyze this data using Snowflake's data warehousing capabilities and requires near-real-time updates. Which command should you use to load this real-time data into Snowflake, considering the requirement for continuous data updates?

1. CREATE STREAM

2. MERGE INTO

3. INSERT INTO

4. COPY INTO

16. You're designing a Snowflake solution for a large multinational company with multiple business units that operate in different regions. You need to ensure that the solution is optimized for performance and cost. Which of the following features of Snowflake should you employ to reduce the computational cost and improve performance for querying large datasets?

1. Use Snowflake's time-travel feature to cache frequently queried datasets.

2. Place all data in a single database to reduce cross-database query costs.

3. Use Snowflake's multi-cluster warehouses for each business unit.

4. Store all data in VARIANT columns to reduce data transformation costs.

17. Your organization frequently receives XML data files containing sales information from various vendors. You need to load this XML data into a Snowflake table for further analysis. What is the most suitable approach for handling this XML data loading scenario?

1. Use the XML PIPE feature to create an XML pipe that directly loads the XML data into the Snowflake table.

2. Pre-process the XML files externally to convert them into CSV or JSON format before loading into Snowflake using the COPY INTO statement.

3. Use the INSERT INTO statement to manually extract data from the XML files and populate the Snowflake table using XML functions.

4. Use the VARIANT data type to store the XML data directly in a Snowflake table column, and then use XML functions to query and manipulate the data.

18. After sharing a database with a consumer account, the consumer reported that they're unable to view the data. You verified that the share was set up correctly on your end. Which of the following could be a potential reason for the issue?

1. The shared data is stored in a Snowflake-managed encryption environment different from the consumer's.

2. The share was not associated with a Snowflake region compatible with the consumer's account.

3. The Snowflake warehouse used by the consumer is not sized appropriately.

4. The consumer has not created a database from the share.

19. You are working with a large dataset containing customer orders in a Snowflake data warehouse. The orders table has billions of rows, and your task is to retrieve the top 10 customers who have made the highest total purchase amounts. The query you have initially written is taking a long time to execute. What strategies could you employ to optimize the query performance?

1. Rewrite the query to use a subquery with a LIMIT clause to retrieve only the top 10 rows.

2. Create an index on the customer ID column in the orders table.

3. Use materialized views to precompute the top 10 customers' total purchase amounts.

4. Increase the size of the virtual warehouse to allocate more resources for query processing.

20. Your marketing team is consolidating user feedback for product analysis. The data comprises:  1. User's sentiment score ranging between -1 (very negative) to 1 (very positive), with multiple decimal points for precision. 2. Feedback text, which may vary in length but can be lengthy. 3. Timestamp detailing when the feedback was given in local time, inclusive of time zones. 4. A set of key-value pairs where keys are product features and values are user ratings for those features. To maintain precision, allow efficient querying, and optimize for storage, which data types should you employ for this dataset in Snowflake?

1. FLOAT for sentiment score, TEXT for feedback text, TIMESTAMP_LTZ for timestamp, and OBJECT for key-value pairs.

2. DECIMAL for sentiment score, STRING for feedback text, TIMESTAMP_NTZ for timestamp, and ARRAY for key-value pairs.

3. NUMBER for sentiment score, VARCHAR for feedback text, TIMESTAMP_TZ for timestamp, and VARIANT for key-value pairs.

4. FLOAT for sentiment score, VARCHAR for feedback text, TIMESTAMP for timestamp, and MAP for key-value pairs.

21. You're a data architect working for an international financial institution. To ensure high security for your Snowflake deployment, which of the following is the primary advantage of using Snowflake's Virtual Private Snowflake (VPS) regarding network security?

1. VPS ensures that Snowflake runs on a dedicated and isolated environment on the cloud provider of your choice.

2. VPS allows you to integrate third-party firewall solutions directly with Snowflake.

3. VPS provides automatic data anonymization before loading into Snowflake.

4. VPS encrypts data twice, once by Snowflake and once by the cloud provider.

22. Your company is developing a complex ETL pipeline that ingests data into Snowflake at irregular intervals. There are concerns about the increasing storage costs. Which of the following statements best describes how Snowflake's storage billing works and how it might affect your scenario?

1. Snowflake's storage costs are solely based on the compressed size of the active data and do not account for time-travel or fail-safe.

2. Snowflake charges for the total size of data, including all duplicates and historical data, stored in micro-partitions.

3. Snowflake charges storage costs only when data is accessed or queried, and dormant data incurs no charges.

4. Snowflake bills for storage on a per-query basis, so irregular ingests won't have an impact on storage costs.

23. Your organization deals with large datasets, often in the order of several terabytes per file, that need to be loaded into Snowflake for analysis. The files are stored in a cloud object storage system. What concepts and best practices should you consider when dealing with such large file sizes during data loading in Snowflake?

1. Split the large files into smaller chunks and load them in parallel using Snowflake's COPY INTO command.

2. Compress the large files using any compression method, as file size doesn't impact data loading performance.

3. Convert the large files into a single binary format to streamline the loading process.

4. Load the files sequentially using the PUT command, as parallel loading is not recommended for large files.

24. Your team is adopting Snowpark for complex data transformations in Snowflake. However, they are familiar with Python and want to leverage existing Python libraries for some transformations. How would you best integrate Snowpark with these Python libraries for the required operations?

1. Implement the libraries directly within Snowpark, as Snowpark natively supports all Python libraries.

2. Use Snowflake's External Functions to call out to a Python service that applies the transformation using the desired library, then integrate the results back using Snowpark.

3. Serialize the DataFrame in Snowpark, send it to an external Python service for transformation, and then re-import the transformed data back into Snowflake using Snowpark.

4. Convert all Python code to Java or Scala and use native Snowpark functions for transformations.

25. A financial services company relies on Snowflake to store and analyze sensitive financial data. Data loss and unauthorized access must be prevented at all costs. The company is interested in understanding Snowflake's capabilities for continuous data protection and encryption. How does Snowflake ensure continuous data protection and data encryption for sensitive financial data stored in its platform?

1. Snowflake's Time Travel feature provides encryption for historical data

2. Snowflake only offers data encryption at rest, not in transit

3. Snowflake relies on periodic manual backups

4. Snowflake's Fail-Safe captures data changes in real-time and data is encrypted at rest and in transit

26. Your company wants to share a view named customer_insights from its Snowflake data warehouse with a partner company without copying or moving data. This view does not include any personally identifiable information, but you must ensure that the partner company can only query the data without altering it. Which of the following steps should you take to achieve this?

1. Enable Snowflake Replication for the view and replicate it to the partner company's Snowflake account.

2. Create a read-only role and share the view via Snowflake Data Marketplace.

3. Clone the customer_insights view and then provide direct access to the partner company's Snowflake account.

4. Create a share of the view and grant the partner company's role access to it.

5. Export the view's data to an S3 bucket and provide the partner company with the S3 bucket's path.

27. Your organization needs to ensure compliance with data privacy regulations and control access to sensitive information stored in Snowflake. At the same time, you need to optimize performance for different business units. How can you configure warehouse settings and access controls to achieve these goals effectively?

1. Implement fine-grained access controls at the virtual warehouse level to restrict data access while optimizing performance.

2. Use a single virtual warehouse with role-based permissions to control access and performance for all business units.

3. Assign the same level of access to all users to maintain a uniform data security and performance experience.

4. Configure automatic scaling for virtual warehouses to dynamically adjust resources for different business units.

28. A newly appointed Data Security Officer at your organization is assessing the default roles in Snowflake. They want to ensure that a set of users can manage stages, file formats, and sequences but should not be able to create or modify virtual warehouses. Which default role in Snowflake best suits this requirement?

1. FULL_ACCESS

2. LOADER

3. USER

4. SECURITYADMIN

5. SYSADMIN

29. You have a Snowflake task named data_sync_task which calls a stored procedure every day. Lately, the task has been failing due to data inconsistencies. You want to ensure that if the task fails three consecutive times, it automatically pauses itself. How can you achieve this?

1. ALTER TASK data_sync_task SET RETRY_COUNT = 3;

2. ALTER TASK data_sync_task SET FAILURE_COUNT = 3 THEN ACTION = 'PAUSE';

3. ALTER TASK data_sync_task SET ERROR_COUNT_TO_PAUSE = 3;

4. ALTER TASK data_sync_task SET MAX_FAILURES = 3;

30. You are a data engineer responsible for optimizing the performance of a complex data processing pipeline in Snowflake. The pipeline involves multiple stages of data transformation and aggregation, and you have noticed that the overall query performance has been deteriorating over time. After analyzing the situation, you suspect that virtual warehouse sizing might be a contributing factor. What is the potential impact of choosing an improperly sized virtual warehouse on the performance of the data processing pipeline?

1. Reducing the virtual warehouse size will guarantee optimized query performance.

2. Increasing the virtual warehouse size will always improve query performance.

3. Choosing a smaller virtual warehouse can lead to longer query execution times.

4. The virtual warehouse size does not affect query performance in any way.

31. A global retail company is considering migrating its customer data to Snowflake's cloud platform. The company needs to ensure data privacy, secure sharing, and compliance with various data protection regulations. Which Snowflake feature addresses the need for data encryption at rest and in transit, ensuring secure data sharing and regulatory compliance?

1. Always-On Data Encryption

2. Data Mirage

3. Data Elusion

4. Data Drift

32. As part of a stringent data governance framework, you are tasked with ensuring that personal identification information (PII) is anonymized in Snowflake. However, certain internal roles should be able to view the original data while external roles should only see masked data. How can you accomplish this?

1. By setting up Resource Monitors to restrict access to sensitive data.

2. By cloning the databases for external roles and removing PII from clones.

3. By using Dynamic Data Masking and assigning return conditions based on roles.

4. By enabling Time Travel on tables with PII and reverting to previous non-PII versions for external roles.

33. You are working with a large dataset that needs to be loaded into Snowflake using the COPY command. The dataset is stored in an external stage on Amazon S3. You want to ensure that the load process is optimized for performance while maintaining data integrity. Which command and options should you use in this scenario?

1. COPY INTO table_name FROM @stage_name FILE_FORMAT = (TYPE = 'AVRO');

2. COPY INTO table_name FROM @stage_name FILE_FORMAT = (TYPE = 'JSON');

3. COPY INTO table_name FROM @stage_name FILE_FORMAT = (TYPE = 'CSV');

4. COPY INTO table_name FROM @stage_name FILE_FORMAT = (TYPE = 'PARQUET');

34. You're developing a data transformation pipeline using Snowpark's DataFrame API. The dataset you're working on has a column 'salary' and you want to create a new DataFrame that filters out rows with a salary greater than 100,000. Which of the following Snowpark methods will achieve this?

1. df.where("salary" > 100000)

2. df.filter(col("salary").lessThan(100001))

3. df.select(col("salary").lessThan(100000))

4. df.filter(col("salary").gt(100000))

35. Your organization manages a rapidly growing dataset that includes customer data from various regions and segments. Queries range from simple filters to complex aggregations across different columns. How can Snowflake's multi-clustering feature enhance query performance for this scenario?

1. Multi-clustering allows concurrent execution of multiple queries on the same dataset, improving throughput.

2. Multi-clustering arranges data within the dataset into micro-partitions based on specified columns, improving pruning and query performance.

3. Multi-clustering organizes the data into predefined clusters based on user-defined categories, enhancing data retrieval.

4. Multi-clustering creates separate copies of the dataset, ensuring redundancy and fault tolerance.

36. You're working with a dataset of user activity logs from a mobile app. The dataset contains a VARIANT column named "event_data," which includes information about each user's interactions. You need to calculate the total count of events for each event type across all users. What's the most efficient way to achieve this?

1. Convert the VARIANT column to JSON and use JSON functions to extract the event type, then group and count the events using traditional SQL GROUP BY and COUNT.

2. Use the ARRAY_AGG function to aggregate the "event_data" column by event type, and then apply COUNT to each aggregated array.

3. Utilize the FLATTEN function to transform the "event_data" into rows, extract event types, and then apply GROUP BY and COUNT.

4. Use the AUTO_TRANSFORM feature to transform the VARIANT column into structured data, and then apply GROUP BY and COUNT.

37. You're a Snowflake architect for a large retail chain. The company has different brands across the globe, with each brand having a slightly different product categorization. While all brands share a core product structure, they each have unique attributes in their product datasets. Which schema design would be the most efficient to address this scenario while allowing for optimized queries and data governance?

1. Build a single schema with a unified product table for shared attributes and separate tables linked with foreign keys for brand-specific attributes.

2. Design a separate schema for each brand with specialized tables and attributes.

3. Use semi-structured data types in a single schema to hold all brand data without differentiation.

4. Create a centralized schema with wide tables that include all possible attributes and utilize Snowflake's sparse column capabilities.

38. You are a data engineer monitoring the storage metrics of your Snowflake environment. Your goal is to keep a check on storage costs while ensuring that data is readily available for analysis. Which of the following Snowflake system functions or views will NOT provide you with valuable information on data storage utilization?

1. SHOW WAREHOUSES

2. DATABASE_STORAGE_USAGE_HISTORY

3. STAGE_STORAGE_USAGE_HISTORY

4. QUERY_HISTORY

39. An organization using Snowflake is setting up new users. They want to ensure that a specific group of users can only view, but not modify, the data in a particular schema. Which combination of roles and privileges should be implemented to achieve this?

1. Create a 'ReadOnly' role, grant this role 'SELECT' on the schema, and assign users to this role.

2. Assign users to the 'FULL_ACCESS' role and grant them 'SELECT' on the schema.

3. Create a custom role but grant it 'USAGE' on the schema only.

4. Grant users 'SELECT', 'INSERT', 'UPDATE', and 'DELETE' on the schema.

40. A tech startup is developing a new mobile application, and they are storing user activity data in Snowflake. They want to trigger a machine learning model in real-time every time a new user signs up. Given the real-time requirements and the need for seamless integration with Snowflake, which Snowflake connector would be most appropriate?

1. Snowflake's ODBC Driver

2. Snowflake's Python Connector

3. Snowflake's JDBC Driver

4. Snowflake's External Functions with AWS Lambda

41. Your organization is in the process of designing a data loading strategy for a data warehouse in Snowflake. The data consists of both historical and real-time streaming data. The goal is to ensure accurate and timely data availability for analytics. What concepts and best practices should you consider when loading and integrating historical and real-time data into Snowflake?

1. Load all data into a single table and use Snowflake's automatic time travel to differentiate historical and real-time data.

2. Implement a hybrid approach: load historical data in batch and stream real-time data using Snowflake's external functions.

3. Use Snowflake's STREAM data type to load historical data and real-time data into the same table.

4. Load historical data and real-time data using the same batch loading process for consistency.

42. You have recently assumed the role of a Snowflake architect at a large financial institution. The institution desires to implement a strategy where their global teams can access Snowflake resources without requiring a new set of credentials, but also ensuring only specific data warehouses can be accessed outside of business hours. Which approach should you adopt?

1. Implement Snowflake's Hierarchical Role-Based Access Control (RBAC) and segregate data based on roles with time-based policies.

2. Activate Multi-Factor Authentication (MFA) for Snowflake users accessing data outside business hours.

3. Integrate Snowflake with the organization's existing Single Sign-On (SSO) solution and apply time-based policies for resource access.

4. Use Snowflake's Data Sharing feature to share specific data warehouses with global teams.

43. You are tasked with improving the performance of certain time-series analytics queries in Snowflake. While exploring Snowflake's documentation, you realize that the metadata Snowflake maintains about columns in micro-partitions could be beneficial. Which type of column metadata would be least relevant for optimizing a time-series query that filters data from the past three months?

1. Metadata about the distinct number of values in a timestamp column.

2. Metadata on the minimum and maximum values of a timestamp column.

3. The last time the clustering was performed on the table.

4. The compression algorithm used for the timestamp column.

44. An e-commerce company uses Snowflake to manage its data. They have a large dataset containing information about products, customers, and orders. The company has noticed that a particular query used to generate customer-specific product recommendations is running slower than usual. The company's database administrator decides to analyze the query execution plan to identify performance bottlenecks. Which of the following statements accurately describes an execution plan and its role in query optimization?

1. Execution plans are only useful for simple queries and have limited relevance for complex queries.

2. An execution plan provides insights into the physical storage of data within Snowflake.

3. An execution plan outlines the order of operations that Snowflake's optimizer will use to execute the query.

4. An execution plan is a graphical representation of the query's logical structure.

45. Your organization is looking to implement Snowflake for its data warehousing needs. As the lead architect, you're particularly concerned about the security of user logins, especially for users accessing Snowflake from diverse geographic regions. What approach can you take to ensure robust login security?

1. Enforce password changes every week for all Snowflake users.

2. Ensure all users have the FULL ACCESS role to regularly check and validate their security privileges.

3. Implement IP whitelisting to restrict Snowflake access to specific geographic regions.

4. Enable Multi-Factor Authentication (MFA) for all Snowflake users.

46. Your organization has a multi-petabyte data lake on AWS S3. The analytics team wants to run ad-hoc SQL queries on this data using Snowflake without necessarily moving all the data into Snowflake storage. Which of the following approaches should you take?

1. Use Snowflake's Snowpipe to continuously ingest data into Snowflake tables and then query.

2. Use external tables in Snowflake to reference the data in the S3 data lake.

3. Replicate the S3 data lake into Snowflake's native storage and run queries.

4. Enable caching on Snowflake to store the S3 data temporarily for querying.

47. In a Snowflake Data Cloud scenario, your organization plans to share specific subsets of data with multiple consumers without duplicating the data. Which feature of Snowflake should you use, and how is it best set up?

1. Use Snowflake Data Exchange and allow consumers to directly access your main database.

2. Use Snowflake Replication and create separate databases for each consumer.

3. Use Snowflake Secure Data Sharing and create separate shares for each consumer.

4. Use Snowflake Data Marketplace and sell data subsets to each consumer.

48. You're designing a Snowflake solution for a healthcare provider. It is crucial that data access is both secure and easily auditable. To meet these requirements, which of the following approaches should you prioritize?

1. Using Role-Based Access Control (RBAC) combined with Snowflake's Query History.

2. Using Snowpipe to ingest data in real-time from source systems.

3. Implementing Time Travel on all tables and databases.

4. Storing all sensitive data in VARIANT data type columns.

49. You are a Snowflake administrator. The company has a role hierarchy where RoleA reports to RoleB, and RoleB reports to RoleC. You granted RoleA the SELECT privilege on a table named "Sales". Later, you realized that RoleB shouldn't have access to this table. Which of the following actions can ensure RoleB cannot access "Sales", without affecting RoleA?

1. Deny SELECT privilege explicitly to RoleB.

2. Change the hierarchy so that RoleA directly reports to RoleC.

3. Revoke SELECT privilege from RoleB.

4. Revoke SELECT privilege from RoleC.

50. You're building a data pipeline for a healthcare research project that involves integrating and analyzing medical data from multiple hospitals. The data includes patient records, diagnoses, treatments, and lab results. As part of the pipeline, you need to aggregate patient data to calculate average treatment durations for specific medical conditions. Which Snowflake-supported function type would you use to perform these aggregations?

1. User-Defined Functions (UDFs)

2. Scalar Functions

3. Aggregate Functions

4. Window Functions

51. ABC Corporation manages sensitive financial data and needs to keep track of who accesses the data for compliance purposes. They want to ensure that every access to sensitive data is logged and that certain users can only view the data without making any modifications. Which Snowflake capability would address this requirement?

1. Snowflake Data Masking

2. Snowflake Secure Data Sharing

3. Snowflake Access History

4. Snowflake Row-Level Security

52. Your company's Snowflake environment includes a shared data warehouse where various teams query and analyze large datasets. As the data is frequently accessed, you are exploring ways to improve query performance using Snowflake's features. One option is to leverage the data cache. How does the data cache work and what are its benefits?

1. The data cache enhances network connectivity between Snowflake and external data sources, minimizing data transfer latency.

2. The data cache stores query results in memory, speeding up subsequent identical queries and reducing compute usage.

3. The data cache automatically precomputes aggregations and materialized views, improving the performance of complex analytical queries.

4. The data cache is a physical storage layer that replicates data across multiple regions, providing fault tolerance and disaster recovery.

53. Your organization processes large volumes of data for various clients, and performance is critical to meeting service level agreements. However, you're experiencing occasional query slowdowns and want to proactively address performance bottlenecks. What Snowflake virtual warehouse performance tools can you utilize to diagnose and optimize these issues?

1. Use the Snowflake Query Performance Insight dashboard to track network latency and visualize query execution timelines.

2. Monitor virtual warehouse performance with Workload Performance Views to identify resource utilization and bottlenecks.

3. Apply Query Profiles to automatically rewrite queries for better performance by optimizing SQL syntax.

4. Configure Query Queues for different client workloads to prioritize queries and prevent performance contention.

54. Your organization has recently migrated all its data warehouses to Snowflake. You have various datasets with different sensitivity levels. For a high-sensitivity dataset, which of the following steps should be taken to ensure that data stored in Snowflake is encrypted, access is controlled, and changes are monitored?

1. Use server-side encryption, implement database triggers, and set up continuous replication.

2. Use Transparent Data Encryption (TDE), enforce Multi-Factor Authentication (MFA), and set up auditing.

3. Enable Always-On encryption, integrate with an Identity Provider (IdP) for SSO, and monitor usage with Snowflake's query history.

4. Activate data sharing, enable auto-suspend, and configure Snowpipe.

55. Your organization is building a Snowflake environment. They've defined two teams: Data Engineering (DE) and Business Intelligence (BI). The DE team should be able to create and modify tables, while the BI team should only be able to read the data. Given this setup, which of the following configurations would be the best approach?

1. Grant both teams the 'SYSADMIN' role and depend on user trustworthiness.

2. Grant the BI team the 'FULL_ACCESS' role and the DE team the 'READ_ONLY' role.

3. Create a single role 'ROLE_ALL' and grant 'SELECT', 'CREATE TABLE', and 'MODIFY' to it. Assign all users to this role.

4. Create custom roles 'ROLE_DE' and 'ROLE_BI'. Grant 'SELECT' privilege to 'ROLE_BI' and 'CREATE TABLE', 'MODIFY' and 'SELECT' privileges to 'ROLE_DE'.

56. Your organization uses Snowflake for analyzing a massive dataset. You have a table with billions of rows, and you need to optimize query performance for a variety of filtering conditions. How does micro-partition pruning contribute to query performance in this scenario?

1. Micro-partition pruning enhances data replication across multiple Snowflake regions, ensuring high availability and disaster recovery.

2. Micro-partition pruning physically divides the table into smaller, independent datasets, reducing the need for joins.

3. Micro-partition pruning allows the query planner to eliminate irrelevant partitions from the query execution, minimizing data to be processed.

4. Micro-partition pruning optimizes network bandwidth by compressing data before transmission, reducing data transfer times.

57. You are tasked with creating a Snowflake data pipeline to continuously ingest data from a real-time source into a Snowflake table. The data source provides a stream of JSON messages. You want to design a pipeline using Snowflake's capabilities to achieve this. Which Snowflake component and command should you use to accomplish this?

1. Use the SNOWPIPE feature to automatically load JSON data from the external source into the target table.

2. Use the COPY INTO table_name command with the PATTERN parameter to directly load JSON data from the source into the table.

3. Use the TASKS feature with a CREATE TASK statement and schedule it to run at specific intervals.

4. Use the STREAMS feature to capture the JSON data and then use a MERGE statement to upsert it into the target table.

5. Use the EXTERNAL FUNCTION feature to create a Python UDF that processes the JSON messages and loads them into the table.

58. Your organization collects data from social media platforms for sentiment analysis. The data arrives in JSON format and includes various fields such as post_id, user_id, timestamp, text, and sentiment_score. You need to transform this data for analysis, aggregating the sentiment scores for each user over a specific time period. What would be the most appropriate approach for performing these transformations in Snowflake?

1. Use Snowflake's JavaScript UDFs to load the JSON data, extract the required fields, and aggregate the sentiment scores within the UDF.

2. Load the JSON data into Snowflake directly as a VARIANT column, and use SQL queries to extract and transform the required data.

3. Download the JSON data, perform the necessary transformations using a Python script, and then load the transformed data into Snowflake.

4. Use Snowflake's Data Pipelines to load the JSON data, apply transformations using SQL scripts, and store the results in a separate table for analysis.

59. You have a JSON document containing information about products and their categories in a nested structure. Your goal is to create a tabular representation of the data for analysis. Which SQL statement is appropriate for flattening and extracting data from this JSON document?

1. SELECT product_name, PARSE_JSON(product_data).category AS category FROM products;

2. SELECT product_name, FLATTEN(categories) AS category FROM products;

3. SELECT product_name, JSON_EXTRACT(product_data, '$.category') AS category FROM products;

4. SELECT product_name, category FROM products WHERE category IS NOT NULL;

60. After integrating a BI tool with Snowflake using the ODBC driver, you observe that despite high performance inside Snowflake's UI, there is notable latency when querying from the BI tool. What is the most likely cause for this discrepancy?

1. Snowflake's Data Sharing feature is affecting performance.

2. The ODBC connection string has misconfigured caching parameters.

3. Snowflake's Virtual Warehouse tied to the BI tool is of a smaller size.

4. The ODBC driver is communicating with Snowflake's fallback cloud region.

61. You have a Snowflake stream called products_stream on the products table. An ETL job consumes changes from this stream every night. One morning, you discover that a mistake was made in the ETL job, and you need to revert the products table to its state from two days ago. After reverting using Time Travel, what happens to the data in products_stream?

1. products_stream will be reset and will only start capturing changes from the point of the Time Travel restore.

2. products_stream will contain all changes from the original state to the current state, including the changes made during the two days.

3. The stream will automatically adjust and show the differences between the Time Traveled version of the table and its current state.

4. The stream becomes invalidated and needs to be recreated.

62. You are working with a partner network on Snowflake Data Cloud. You need to ensure that third-party partners can access data in a centralized manner, and at the same time, data providers need to maintain control and governance over their data. Which approach should you consider?

1. Request partners to clone the data into their Snowflake account.

2. Use external stages to allow third-party access.

3. Employ Snowflake Data Exchange for centralized data sharing.

4. Employ Snowflake Managed Private Exchange for restricted and controlled access.

63. Your organization is migrating multiple OLTP systems into Snowflake for unified analytics. Each OLTP system corresponds to different departmental functions. You need to ensure that each department can only access its data while being able to share specific insights across departments. Which setup would be the most appropriate for this scenario in Snowflake?

1. Create separate databases for each OLTP system, shared warehouses, and assign access using roles.

2. Store all OLTP data in one database but separate schemas, one warehouse for all, and manage data using secure UDFs.

3. One shared database for all OLTP systems, separate virtual warehouses, and Snowflake Data Sharing for inter-departmental sharing.

4. Individual databases for each OLTP, separate virtual warehouses, and roles that grant access at the schema level.

64. You are working with a data engineering team that is loading data from an S3 bucket into Snowflake. The team is using Snowflake's pipe functionality to continuously load the data as it lands in the S3 bucket. Which of the following statements about Snowflake pipes is not true?

1. Pipes automatically load data in micro-batches.

2. You can create an auto-ingest pipe that retrieves files automatically from an external location once they are available.

3. Every time a new file is detected in the external location, Snowflake creates a new virtual warehouse to process the data.

4. A notification integration can be set up to allow Snowflake to listen for new data on an external location.

65. Leveraging the Snowflake Data Cloud, your company wishes to maintain high levels of data governance, ensuring compliance and data quality while collaborating with cloud partners. Which of the Snowflake's cloud partner categories is best suited to provide solutions and tools to enforce data lineage, quality, and access controls?

1. Application Development Partners

2. Data Governance Partners

3. Connectivity Service Partners

4. Query Service Partners

66. Your organization is dealing with a massive dataset stored in Snowflake, and you've been tasked with optimizing query performance. A particular query seems to be running slower than expected, impacting the overall system performance. After analyzing the query, you suspect that query optimization using a Query Profile might help. Which of the following statements about Query Profiles is true?

1. Query Profiles are used to create backup copies of queries for disaster recovery purposes.

2. Query Profiles are used to manage user access and privileges to specific queries.

3. Query Profiles provide insights into the execution plan and resource usage of a specific query.

4. Query Profiles are automated optimization strategies that require no manual intervention.

67. A colleague of yours is designing a table in Snowflake and is worried about optimizing storage and reducing costs. She's learned that Snowflake uses micro-partitions for storage. Which of the following is not an accurate statement regarding how micro-partitions can affect storage cost optimization?

1. Larger tables can be automatically split into multiple micro-partitions, but the total uncompressed size of individual micro-partitions generally doesn't exceed 500 MB.

2. Micro-partitions store data in a columnar format, which generally allows for efficient compression.

3. Metadata about each micro-partition, including min and max values, assists in query pruning, reducing compute costs.

4. Micro-partitions automatically adjust their size based on query performance metrics.

68. Your organization has recently onboarded several new business units, each with varying data processing needs. As the Snowflake administrator, you are responsible for setting up the virtual warehouse configurations to ensure optimal query performance while managing costs. How should you approach warehouse sizing for this complex scenario?

1. Set up separate virtual warehouses for each business unit with equal sizes to ensure fair resource allocation.

2. Use automatic scaling for virtual warehouses to dynamically adjust resources based on incoming queries.

3. Create a single large virtual warehouse to accommodate the combined query needs of all business units.

4. Allocate smaller virtual warehouses for business units with less demanding queries and larger ones for high-demand units.

69. Which query performance optimization approach in Snowflake is best suited for improving the efficiency of complex analytical queries involving multiple joins and aggregations?

1. Materialized Views

2. Snowflake Query Acceleration (SQA)

3. Virtual Warehouses

4. External Functions

70. DEF Healthcare is a large healthcare organization dealing with sensitive patient data. They want to implement a data governance solution in Snowflake that ensures compliance with HIPAA regulations while enabling various departments to collaborate on data analysis. Which Snowflake data governance capability would best suit this scenario?

1. Data Replication

2. Cross-Database Joins

3. Private Snowflake Data Exchange

4. Secure Data Sharing

71. You're designing a data architecture for a modern e-commerce platform. The platform expects high volumes of transactions and needs a system to capture clickstream data in real-time, then promptly load it into Snowflake. Which Snowflake connector would be the most suitable for this requirement?

1. Snowflake's Kafka Connector

2. Snowflake's Python Connector

3. Snowflake's JDBC Connector

4. Snowflake's Spark Connector

72. You've been hired as a Snowflake consultant for a multinational company. They've just transitioned to Snowflake and are looking to optimize their data operations. When outlining key Snowflake tools and user interfaces, which of the following is not an actual component or feature associated with Snowflake's main interface?

1. Data Paintbrush

2. History tab

3. Snowsight

4. Worksheets

73. Your organization is building a data warehouse solution using Snowflake. The data engineering team is tasked with regularly updating a summary table that aggregates data from various source tables. The summary table needs to be completely refreshed every day while ensuring the highest performance and minimal disruption to concurrent queries. Which command should be used to achieve this requirement?

1. Use the UPDATE statement with joins to update the existing rows in the summary table.

2. Use the INSERT INTO statement with a SELECT subquery to populate the summary table.

3. Use the MERGE statement to merge the data from source tables into the summary table.

4. Use the INSERT OVERWRITE statement to overwrite the summary table with new aggregated data.

74. Your organization is migrating its on-premises data warehouse to Snowflake's cloud-based data warehousing solution. The current data warehouse has multiple departments with their own schemas and datasets. The goal is to ensure optimal performance and isolation for each department's queries and data. What design strategies could you employ to achieve this in Snowflake?

1. Create a single virtual warehouse with maximum resources and assign it to all departments for uniform performance.

2. Combine all departmental data into a single large table and use views to segment data based on departmental attributes.

3. Use a single schema for all departments to simplify management and avoid query complexity.

4. Assign a separate virtual warehouse to each department, sized according to their workload needs.

75. A financial institution is using Snowpark to analyze transactional data for fraud detection. The process requires applying multiple transformations and aggregations on the data. However, the data contains millions of records and needs frequent updates. To ensure optimal performance in Snowpark, which strategy would be most effective?

1. Apply the transformations using Snowflake-native SQL before using Snowpark for more complex operations.

2. Use multiple smaller virtual warehouses for processing the data and then consolidate the results.

3. Load the data into Snowpark, cache it, and apply transformations directly.

4. Split the data into chunks, apply transformations in Snowpark sequentially on each chunk, and then consolidate.

76. In a Snowflake environment, you're using Snowpark to manipulate data stored in a table called transactions. This table has a column named purchase_date that stores data in DATE format. You want to leverage Snowpark to extract the month from each purchase date and create a new column named purchase_month. Which of the following Snowpark methods will achieve this?

1. df.newColumn("purchase_month", month(df.get("purchase_date")))

2. df.withColumn("purchase_month", month(df("purchase_date")))

3. df.add("purchase_month", month(df.column("purchase_date")))

4. df.addColumn("purchase_month", extractMonth(df.col("purchase_date")))

77. ABC Corp is a multinational company dealing with sensitive customer data. They need to ensure both data protection and data sharing across their global offices. They want to adopt a solution that provides granular control over data access, allows secure data sharing, and ensures compliance with data regulations. Which of the following options best addresses the complex scenario for both data protection and data sharing?

1. Use Snowflake's Secure Data Sharing to share selected data sets while maintaining control over access.

2. Create separate roles for each office with predefined data access, utilizing Snowflake's dynamic data masking.

3. Share data using direct database links, allowing each office to manage its data independently.

4. Utilize Snowflake's default encryption settings and rely on network security for data sharing.

5. Implement a single global role with full access to all data, controlled by the IT team.

FAQs

1. What is the SnowPro Core Certification COF-C02?

It is the updated Snowflake certification that validates knowledge of Snowflake architecture, features, and basic implementation skills.

2. How do I become SnowPro Core certified?

You need to study Snowflake concepts, register for the COF-C02 exam through the Snowflake certification portal, and pass the test.

3. What are the prerequisites for the SnowPro Core Certification exam?

There are no mandatory prerequisites, but basic knowledge of SQL and cloud data warehousing is recommended.

4. How much does the SnowPro Core COF-C02 exam cost?

The exam fee is $175 USD.

5. How many questions are on the SnowPro Core Certification exam?

The exam has 100 multiple-choice and multiple-select questions.

6. What is the passing score for the SnowPro Core COF-C02 exam?

You need a minimum score of 750 out of 1000.

7. How long is the SnowPro Core Certification exam?

The exam duration is 115 minutes.

8. What topics are covered in the SnowPro Core COF-C02 exam?

It covers Snowflake architecture, data loading/unloading, security, performance, account management, and use cases.

9. How difficult is the SnowPro Core Certification exam?

It is considered moderately challenging, requiring both theoretical and practical knowledge.

10. How long does it take to prepare for the SnowPro Core COF-C02 exam?

Most candidates prepare in 6–8 weeks, depending on prior Snowflake experience.

11. Are there any SnowPro Core COF-C02 sample questions or practice tests available?

Yes, Snowflake provides sample questions, and CertiMaan offers practice tests and dumps.

12. What is the validity period of the SnowPro Core Certification?

The certification is valid for 2 years.

13. Can I retake the SnowPro Core COF-C02 exam if I fail?

Yes, you can retake after a 14-day waiting period.

14. What jobs can I get with a SnowPro Core Certification?

You can work as a Snowflake Developer, Data Engineer, Data Analyst, or Cloud Data Specialist.

15. How much salary can I earn with a SnowPro Core Certification?

Certified professionals typically earn between $90,000–$130,000 annually, depending on role and region.

16. Is the SnowPro Core Certification worth it?

Yes, it is in high demand as Snowflake continues to dominate the cloud data warehousing market.

17. What is the difference between COF-C01 and COF-C02 SnowPro Core exams?

• COF-C01: Older version, retired in 2023.

• COF-C02: Current version with updated Snowflake features and architecture.

18. What are the best study materials for the SnowPro Core COF-C02 exam?

Use Snowflake documentation, official training courses, and CertiMaan practice resources.

19. Does Snowflake provide official training for the SnowPro Core Certification?

Yes, Snowflake offers online training, on-demand courses, and documentation for preparation.

20. Where can I register for the SnowPro Core COF-C02 exam?

You can register on the Snowflake certification portal hosted by the official testing partner.