Databricks Certified Data Engineer Associate Certification Exam Questions

The Databricks Certified Data Engineer Associate Certification is one of the most recognized credentials for professionals working with modern data engineering, big data pipelines, cloud analytics, and lakehouse architecture. This certification validates your ability to work with core data engineering concepts in the Databricks ecosystem, including data ingestion, transformation, ETL workflows, Delta Lake, Apache Spark, data governance, and workflow optimization. As organizations increasingly adopt cloud-based analytics platforms and scalable data processing solutions, certified Databricks professionals are becoming highly valuable across industries such as finance, healthcare, retail, telecommunications, and AI-driven enterprises.

The Databricks Certified Data Engineer Associate exam is ideal for aspiring data engineers, cloud professionals, analytics engineers, ETL developers, and big data practitioners who want to demonstrate practical knowledge of the Databricks Lakehouse Platform. It is also highly beneficial for professionals transitioning into modern data engineering roles from traditional database or BI backgrounds. By earning this certification, candidates can showcase their understanding of distributed data processing, SQL analytics, Spark fundamentals, Delta Lake operations, workflow orchestration, and performance optimization techniques used in enterprise-grade data environments.

This page provides carefully structured Databricks Certified Data Engineer Associate certification exam questions, preparation guidance, and practice-focused learning support to help certification aspirants strengthen conceptual clarity and improve exam readiness. These practice questions are designed to simulate real exam scenarios and help candidates become familiar with the certification pattern, technical domains, and question complexity commonly encountered during the actual exam.

Using practice questions strategically can significantly improve certification preparation. They help identify weak areas, improve time management skills, reinforce theoretical understanding through practical examples, and build confidence before the final exam. Instead of relying only on theoretical study materials, candidates who regularly practice scenario-based questions often develop stronger analytical thinking and better problem-solving abilities required for modern data engineering environments.

Whether you are preparing for your first Databricks certification or looking to validate your existing data engineering skills, this Databricks Certified Data Engineer Associate exam questions page is designed to support your certification journey with practical, search-optimized, and learner-focused content tailored for real-world exam preparation.

Table of Contents

• Databricks Certified Data Engineer Associate Exam Details Table

• How to Prepare Databricks Certified Data Engineer Associate Certification?

• Career Benefits of Databricks Certified Data Engineer Associate Certification

• Exam Tips Of Databricks Certified Data Engineer Associate Certification

• FAQs - Databricks Certified Data Engineer Associate Certification

Databricks Certified Data Engineer Associate — Exam Details

Exam Detail	Information
Certification	Databricks Certified Data Engineer Associate
Provider	Databricks
Exam Code	Data Engineer Associate
Certification Level	Associate Level
Exam Format	Multiple-choice and multiple-select questions
Total Questions	Approximately 45 Questions
Exam Duration	90 Minutes
Passing Score	Approximately 70%
Delivery Method	Online Proctored Exam
Exam Language	English
Recommended Experience	6+ months of hands-on Databricks and Apache Spark experience
Core Technologies Covered	Delta Lake, Apache Spark, SQL, ETL Pipelines, Lakehouse Architecture
Exam Focus Areas	Data Processing, Data Transformation, Workflow Management, Data Governance, Performance Optimization
Difficulty Level	Intermediate
Target Audience	Data Engineers, ETL Developers, Cloud Data Professionals, Analytics Engineers
Certification Validity	Subject to Databricks certification policy updates
Official Platform	Databricks Lakehouse Platform
Recommended Preparation	Hands-on labs, practice exams, Spark SQL exercises, Delta Lake operations, workflow implementation
Industry Relevance	High demand in cloud analytics, big data engineering, AI, and enterprise data modernization projects

This certification exam evaluates a candidate’s practical understanding of modern data engineering workflows within the Databricks ecosystem. The exam emphasizes real-world implementation skills related to scalable data pipelines, data transformation techniques, Spark operations, Delta Lake management, and cloud-based analytics environments. Candidates preparing for this certification should focus heavily on hands-on practice, SQL-based transformations, Spark processing concepts, and Databricks workspace operations to improve real exam readiness.

How to Prepare for the Databricks Certified Data Engineer Associate Certification

Preparing for the Databricks Databricks Certified Data Engineer Associate Certification requires a balanced strategy that combines conceptual learning, hands-on practice, workflow understanding, and consistent mock exam preparation. Since the certification focuses heavily on practical data engineering operations within the Databricks Lakehouse Platform, candidates should avoid relying only on theoretical study materials. Real-world implementation experience plays a major role in exam success.

Start by building a strong understanding of the core exam domains, including Apache Spark fundamentals, Spark SQL, Delta Lake operations, ETL pipeline development, workflow orchestration, and data transformation techniques. Candidates should become comfortable working with notebooks, clusters, jobs, tables, and data ingestion workflows inside the Databricks environment. Understanding how structured and semi-structured data is processed in distributed systems is especially important for this certification.

Hands-on practice is one of the most effective preparation methods for this exam. Spend time creating and managing ETL pipelines, running Spark transformations, optimizing queries, and working with Delta tables. Practical exposure to batch processing, streaming concepts, schema enforcement, partitioning, and performance optimization can significantly improve both confidence and exam readiness.

Mock exams and certification practice questions are equally important during preparation. Practice exams help candidates:

• Understand the exam pattern

• Improve time management

• Identify weak technical areas

• Strengthen scenario-based problem-solving skills

• Build familiarity with real certification question styles

A smart preparation strategy should also include regular revision of:

• Spark SQL commands

• Delta Lake features

• Data governance concepts

• Data quality techniques

• Workflow scheduling

• Data pipeline troubleshooting

Candidates preparing for the exam should dedicate additional attention to understanding how Databricks handles scalable analytics, distributed computing, and modern lakehouse architecture. Many exam questions are scenario-oriented and test practical decision-making rather than simple memorization.

To improve preparation efficiency, create a structured study schedule that combines:

1. Concept learning

2. Hands-on labs

3. Practice questions

4. Mock exams

5. Weak-area analysis

Reviewing incorrect answers during mock tests is extremely valuable because it helps uncover conceptual gaps and improves long-term retention. Consistent practice combined with practical implementation experience is one of the best ways to prepare confidently for the Databricks Certified Data Engineer Associate certification exam.

Reviewed & Verified by CertiMaan Certification Support Team

This Databricks Certified Data Engineer Associate certification exam questions page has been carefully reviewed by the CertiMaan Certification Support Team to ensure accuracy, technical relevance, and alignment with the latest Databricks certification objectives. The questions, explanations, and preparation guidance available on this page are designed to help certification aspirants strengthen practical data engineering knowledge, improve analytical thinking, and prepare confidently for real-world certification scenarios within the Databricks Lakehouse ecosystem.

Our review process focuses on validating exam-topic relevance, technical correctness, and alignment with current industry practices used in cloud-based data engineering environments. The content has been structured to support learners preparing for modern data engineering workflows involving Apache Spark, Delta Lake, scalable ETL pipelines, distributed processing, data transformation, and workflow orchestration. Each practice area is evaluated from both certification and real-world implementation perspectives to improve learning effectiveness and exam readiness.

The CertiMaan Certification Support Team regularly reviews evolving technologies, certification blueprint updates, and modern data engineering practices to maintain high-quality, educational, and preparation-focused content for learners, working professionals, cloud engineers, and aspiring data specialists. Our objective is to provide trustworthy and practical certification preparation support that helps candidates understand not only the exam pattern but also the underlying concepts required in enterprise data engineering projects.

This review methodology emphasizes:

• Technical accuracy

• Conceptual clarity

• Certification relevance

• Practical implementation understanding

• Scenario-based learning alignment

• Industry-focused preparation strategies

Topics Reviewed:

 Apache Spark Fundamentals, Spark SQL, Delta Lake, Data Pipelines, ETL Workflows, Databricks Lakehouse Architecture, Data Governance, Data Transformation, Workflow Orchestration, Data Processing Optimization, Structured & Semi-Structured Data Handling, Distributed Computing Concepts, Cloud Data Engineering Best Practices.

Career Benefits of Databricks Certified Data Engineer Associate Certification

The Databricks Databricks Certified Data Engineer Associate Certification can play a significant role in advancing a professional career in modern data engineering, cloud analytics, and big data processing. As organizations continue moving toward cloud-native architectures and large-scale data platforms, the demand for skilled data engineers with hands-on Databricks expertise is increasing rapidly across industries worldwide.

One of the biggest advantages of earning this certification is skill validation. The certification demonstrates that a candidate understands core data engineering concepts such as Apache Spark processing, Delta Lake operations, ETL pipeline development, data transformation, workflow orchestration, and scalable analytics. Employers often look for professionals who can work confidently with distributed data systems and modern lakehouse environments, making this certification highly valuable for career growth.

This certification is especially beneficial for professionals working in or transitioning into roles such as:

• Data Engineer

• Big Data Engineer

• Cloud Data Engineer

• ETL Developer

• Analytics Engineer

• Data Platform Engineer

• Data Integration Specialist

• Spark Developer

The Databricks ecosystem is widely used in enterprise analytics, machine learning workflows, AI platforms, and cloud-based data modernization projects. Because of this, certified professionals may find opportunities in sectors such as banking, healthcare, retail, e-commerce, telecommunications, logistics, cybersecurity, and AI-driven organizations.

Another important career benefit is practical technology exposure. Preparing for this certification helps candidates gain hands-on experience with:

• Distributed computing concepts

• Spark SQL operations

• Data pipeline optimization

• Delta Lake management

• Batch and streaming workflows

• Cloud-scale data processing

• Lakehouse architecture principles

These are highly transferable skills that align with modern enterprise data engineering requirements.

For professionals already working with cloud platforms like Amazon Web Services AWS, Microsoft Azure, or Google Cloud Google Cloud, the Databricks certification can strengthen multi-platform data engineering capabilities and improve technical credibility in cloud transformation projects.

The certification also supports long-term professional development by encouraging deeper understanding of scalable analytics systems and real-world data engineering practices. Candidates who combine this certification with practical project experience often improve their confidence in handling enterprise-grade data workflows, optimization challenges, and analytics infrastructure responsibilities.

From an industry recognition perspective, Databricks certifications are increasingly viewed as valuable credentials within modern data engineering and analytics ecosystems. As more companies adopt lakehouse platforms and AI-driven data architectures, certified professionals may gain stronger visibility in technical hiring processes and enterprise cloud initiatives.

40+ Databricks Certified Data Engineer Associate Certification Sample Questions

1. Which tool is used by Auto Loader to process data incrementally?

1. Spark Structured Streaming

2. Databricks SQL

3. Checkpointing

4. Unity Catalog

2. Which two components function in the DB platform architecture’s control plane? (Choose two.)

1. Virtual Machines

2. Compute Orchestration

3. Compute

4. Unity Catalog

5. Serverless Compute

3. How can Git operations must be performed outside of Databricks Repos?

1. Merge

2. Pull

3. Commit

4. Clone

4. A dataset has been defined using Delta Live Tables and includes an expectations clause: CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

1. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log

2. Records that violate the expectation cause the job to fail

3. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table

4. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log

5. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset

5. A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level. Which of the following tools can the data engineer use to solve this problem?

1. Delta Lake

2. Delta Live Tables

3. Auto Loader

4. Unity Catalog

6. An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release. Which approach can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

1. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint

2. They can set the query’s refresh schedule to end after a certain number of refreshes

3. They can set the query’s refresh schedule to end on a certain date in the query scheduler

4. They can set a limit to the number of individuals that are able to manage the query’s refresh schedule

7. A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Development mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

1. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

2. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing

3. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

4. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused

5. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down

8. A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task. Which approach can the data engineer use to set up the new task?

1. They can create a new task in the existing Job and then add the original task as a dependency of the new task

2. They can create a new job from scratch and add both tasks to run concurrently

3. They can create a new task in the existing Job and then add it as a dependency of the original task

4. They can clone the existing task in the existing Job and update it to run the new notebook

9. A data engineer has been given a new record of data: id STRING = 'a1' rank INTEGER = 6 rating FLOAT = 9.4 Which SQL commands can be used to append the new record to an existing Delta table my_table?

1. UPDATE VALUES ('a1', 6, 9.4) my_table

2. UPDATE my_table VALUES ('a1', 6, 9.4)

3. INSERT INTO my_table VALUES ('a1', 6, 9.4)

4. INSERT VALUES ('a1', 6, 9.4) INTO my_table

10. A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start. Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

1. They can configure the clusters to autoscale for larger data sizes

2. They can configure the clusters to be single-node

3. They can use jobs clusters instead of all-purpose clusters

4. They can use endpoints available in Databricks SQL

5. They can use clusters that are from a cluster pool

11. Which of the following commands will return the location of database customer360?

1. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};

2. DESCRIBE LOCATION customer360;

3. DESCRIBE DATABASE customer360;

4. USE DATABASE customer360;

5. DROP DATABASE customer360;

12. What describes the relationship between Gold tables and Silver tables?

1. Gold tables are more likely to contain a less refined view of data than Silver tables

2. Gold tables are more likely to contain aggregations than Silver tables

3. Gold tables are more likely to contain truthful data than Silver tables

4. Gold tables are more likely to contain valuable data than Silver tables

13. A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task. Which of the following approaches can the data engineer use to set up the new task?

1. They can create a new job from scratch and add both tasks to run concurrently

2. They can create a new task in the existing Job and then add the original task as a dependency of the new task

3. They can create a new task in the existing Job and then add it as a dependency of the original task

4. They can clone the existing task to a new Job and then edit it to run the new notebook

5. They can clone the existing task in the existing Job and update it to run the new notebook

14. A data engineer is managing a data pipeline in Databricks, where multiple Delta tables are used for various transformations. The team wants to track how data flows through the pipeline, including identifying dependencies between Delta tables, notebooks, jobs, and dashboards. The data engineer is utilizing the Unity Catalog lineage feature to monitor this process. How does Unity Catalog’s data lineage feature support the visualization of relationships between Delta tables, notebooks, jobs, and dashboards?

1. Unity Catalog lineage provides an interactive graph that tracks dependencies between tables and notebooks but excludes any job-related dependencies or dashboard visualizations

2. Unity Catalog lineage only supports visualizing relationships at the table level and does not extend to notebooks, jobs, or dashboards

3. Unity Catalog provides an interactive graph that visualizes the dependencies between Delta tables, notebooks, jobs, and dashboards, while also supporting column-level tracking of data transformations

4. Unity Catalog lineage visualizes dependencies between Delta tables, notebooks, and jobs, but does not provide column-level tracing or relationships with dashboards

15. Which of the following commands will return the number of null values in the member_id column?

1. SELECT count(member_id) FROM my_table;

2. SELECT null(member_id) FROM my_table;

3. SELECT count_if(member_id IS NULL) FROM my_table;

4. SELECT count(member_id) - count_null(member_id) FROM my_table;

16. A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository. Which Git operation does the data engineer need to run to accomplish this task?

1. Push

2. Merge

3. Pull

4. Clone

17. A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells. Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?

1. They can add %sql to the first line of the cell

2. They can change the default language of the notebook to SQL

3. They can simply write SQL syntax in the cell

4. It is not possible to use SQL in a Python notebook

5. They can attach the cell to a SQL endpoint rather than a Databricks cluster

18. An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release. Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

1. They can set a limit to the number of individuals that are able to manage the query’s refresh schedule

2. They can set the query’s refresh schedule to end on a certain date in the query scheduler

3. They can set the query’s refresh schedule to end after a certain number of refreshes

4. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint

5. They cannot ensure the query does not cost the organization money beyond the first week of the project’s release

19. A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data. Which of the following relational objects should the data engineer create?

1. Delta Table

2. View

3. Temporary view

4. Spark SQL Table

20. Identify how the count_if function and the count where x is null can be used Consider a table random_values with below data. What would be the output of below query? select count_if(col > 1) as count_a. count(*) as count_b.count(col1) as count_c from random_values col1 0 1 2 NULL - 2 3

1. 3 6 6

2. 4 6 5

3. 4 6 6

4. 3 6 5

Exam Tips for the Databricks Certified Data Engineer Associate Certification

Preparing for the Databricks Databricks Certified Data Engineer Associate exam becomes much easier when candidates combine conceptual learning with practical exam-oriented strategies. Since the certification focuses on real-world data engineering workflows, understanding the exam pattern and improving hands-on problem-solving skills are extremely important for achieving a strong score.

One of the most effective exam preparation tips is to thoroughly understand the core certification domains instead of memorizing isolated concepts. The exam frequently tests practical implementation scenarios involving Spark SQL, Delta Lake, ETL pipelines, workflow orchestration, data transformations, and distributed processing. Candidates should focus on understanding how these technologies work together inside the Databricks Lakehouse Platform.

Time management is another critical factor during the exam. Because many questions are scenario-based, spending too much time on a single question can affect overall performance. During practice sessions, train yourself to:

• Read questions carefully

• Identify key technical keywords

• Eliminate incorrect options quickly

• Manage time consistently across all sections

Mock exams and practice questions can significantly improve exam confidence. Regular practice helps candidates become familiar with:

• Question difficulty levels

• Common certification patterns

• Technical terminology

• Scenario interpretation

• Decision-making under time pressure

Candidates should carefully review incorrect answers from mock tests rather than simply checking final scores. Weak-area analysis is one of the best ways to improve preparation quality. If you repeatedly struggle with Delta Lake commands, Spark transformations, or workflow concepts, dedicate extra revision time to those areas.

Hands-on practice is especially important for this certification. Spend time working directly with:

• Databricks notebooks

• Spark SQL queries

• Data ingestion workflows

• Delta tables

• Job scheduling

• Data transformations

• Cluster management basics

Many successful candidates use a preparation strategy that combines:

1. Official documentation study

2. Hands-on implementation

3. Scenario-based practice questions

4. Timed mock exams

5. Revision sessions

Avoid rushing through the exam preparation process. Instead of trying to memorize every command, focus on understanding practical data engineering workflows and how Databricks optimizes large-scale analytics operations.

Before the exam day:

• Get proper rest

• Ensure a stable internet connection for online proctored exams

• Read each question calmly

• Avoid overthinking simple concepts

• Trust your preparation process

Confidence often comes from consistent practice and conceptual clarity. Candidates who regularly work with Spark operations, ETL pipelines, and Databricks workflows generally perform better because the certification strongly emphasizes practical understanding over theoretical memorization alone.

21. What describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

1. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally

2. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static

3. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static

4. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations

22. A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True. Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

1. if day_of_week = 1 & review_period: = "True":

2. if day_of_week = 1 and review_period = "True":

3. if day_of_week = 1 and review_period:

4. if day_of_week == 1 and review_period:

23. Which two conditions are applicable for governance in Databricks Unity Catalog? (Choose two.)

1. Both catalog and schema must have a managed location in Unity Catalog provided metastore is not associated with a location

2. You can have more than 1 metastore within a databricks account console but only 1 per region

3. If metastore is not associated with location, it’s mandatory to associate catalog with managed locations

4. You can have multiple catalogs within metastore and 1 catalog can be associated with multiple metastore

5. If catalog is not associated with location, it’s mandatory to associate schema with managed locations

24. A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location. Which of the following data entities should the data engineer create?

1. Function

2. Table

3. Database

4. View

5. Temporary view

25. A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary. Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

1. They can ensure the dashboard’s SQL endpoint is not one of the included query’s SQL endpoint

2. They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints

3. They can turn on the Auto Stop feature for the SQL endpoint

4. They can set up the dashboard’s SQL endpoint to be serverless

26. A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped. Which of the following approaches can the data engineer take to identify the table that is dropping the records?

1. They cannot determine which table is dropping the records

2. They can set up separate expectations for each table when developing their DLT pipeline

3. They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors

4. They can set up DLT to notify them via email when records are dropped

5. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics

27. A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions. In which location can the data engineer review their permissions on the table?

1. Catalog Explorer

2. Dashboards

3. Repos

4. Jobs

28. A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository. Which of the following Git operations does the data engineer need to run to accomplish this task?

1. Pull

2. Merge

3. Push

4. Commit

5. Clone

29. An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results. Which of the following approaches can the manager use to ensure the results of the query are updated each day?

1. They can schedule the query to run every 12 hours from the Jobs UI

2. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL

3. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL

4. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL

30. A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name. They have the following incomplete code block: ____(f"SELECT customer_id, spend FROM {table_name}") What can be used to fill in the blank to successfully complete the task?

1. spark.table

2. spark.sql

3. spark.delta.sql

4. dbutils.sql

31. Which of the following describes the storage organization of a Delta table?

1. Delta tables are stored in a collection of files that contain only the data stored within the table

2. Delta tables are stored in a single file that contains only the data stored within the table

3. Delta tables are stored in a single file that contains data, history, metadata, and other attributes

4. Delta tables store their data in a single file and all metadata in a collection of files in a separate location

5. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes

32. Identify a scenario to use an external table. A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location. Which table can be created in this scenario?

1. An external table where the location is pointing to specific path in external location

2. An external table where the schema has managed location pointing to specific path in external location

3. A managed table where the catalog has managed location pointing to specific path in external location

4. A managed table where the location is pointing to specific path in external location

33. Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

1. DROP

2. IGNORE

3. APPEND

4. MERGE

5. INSERT

34. A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs. Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

1. Cron syntax

2. pyspark.sql.types.DateType

3. datetime

4. pyspark.sql.types.TimestampType

35. A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values. Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

1. All of the fields had at least one null value

2. Auto Loader only works with string data

3. Auto Loader cannot infer the schema of ingested data

4. JSON data is a text-based format

5. There was a type mismatch between the specific schema and the inferred schema

36. What is the maximum output supported by a job cluster to ensure a notebook does not fail?

1. 10MBs

2. 25MBs

3. 15MBs

4. 30MBs

37. What is used by Spark to record the offset range of the data being processed in each trigger in order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing?

1. Replayable Sources and Idempotent Sinks

2. Checkpointing and Write-ahead Logs

3. Write-ahead Logs and Idempotent Sinks

4. Checkpointing and Idempotent Sinks

38. Which of the following must be specified when creating a new Delta Live Tables pipeline?

1. At least one notebook library to be executed

2. A path to cloud storage location for the written data

3. A location of a target database for the written data

4. A key-value pair configuration

39. Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

1. Silver tables contain a less refined, less clean view of data than Bronze data

2. Silver tables contain aggregates while Bronze data is unaggregated

3. Silver tables contain less data than Bronze tables

4. Silver tables contain more data than Bronze tables

5. Silver tables contain a more refined and cleaner view of data than Bronze tables

40. In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

1. When the location of the data needs to be changed

2. When the source is not a Delta table

3. When the target table is an external table

4. When the target table cannot contain duplicate records

Frequently Asked Questions ( FAQs ) — Databricks Certified Data Engineer Associate Certification

1. What is the Databricks Certified Data Engineer Associate certification?

The Databricks Certified Data Engineer Associate certification validates foundational to intermediate-level skills in data engineering using the Databricks Lakehouse Platform. It focuses on Apache Spark, Delta Lake, ETL pipelines, data transformation, workflow orchestration, and scalable analytics processing.

2. Who should take the Databricks Certified Data Engineer Associate exam?

This certification is ideal for:

• Data Engineers

• ETL Developers

• Analytics Engineers

• Cloud Data Professionals

• Big Data Engineers

• Professionals transitioning into modern data engineering roles

It is especially useful for candidates working with Spark-based data processing and cloud analytics platforms.

3. Is the Databricks Certified Data Engineer Associate exam difficult?

The exam is considered moderately challenging because it tests practical understanding of Spark operations, Delta Lake concepts, ETL workflows, and data engineering best practices. Candidates with hands-on Databricks experience generally find the exam more manageable.

4. What topics are covered in the Databricks Data Engineer Associate exam?

The exam commonly covers:

• Apache Spark fundamentals

• Spark SQL

• Delta Lake

• ETL pipelines

• Data transformation

• Workflow orchestration

• Data governance

• Lakehouse architecture

• Batch and streaming concepts

• Data optimization techniques

5. How many questions are included in the certification exam?

The Databricks Certified Data Engineer Associate exam typically includes around 45 questions presented in multiple-choice and multiple-select formats.

6. How long is the Databricks certification exam?

Candidates usually receive approximately 90 minutes to complete the exam.

7. What is the best way to prepare for the Databricks Certified Data Engineer Associate certification?

A strong preparation strategy should include:

• Official Databricks documentation

• Hands-on Spark practice

• Databricks notebook exercises

• Delta Lake implementation

• Mock exams

• Scenario-based practice questions

• Workflow and ETL pipeline creation

Hands-on learning is extremely important for this certification.

8. Are practice questions useful for Databricks certification preparation?

Yes. Practice questions help candidates:

• Understand the exam pattern

• Improve time management

• Identify weak areas

• Strengthen scenario-based problem-solving

• Build exam confidence

Regular practice can significantly improve certification readiness.

9. Does the exam require coding knowledge?

Basic to intermediate-level coding and query-writing skills are beneficial. Candidates should be comfortable working with Spark SQL, data transformations, notebook operations, and ETL-related logic within Databricks.

10. What is Delta Lake in Databricks?

Delta Lake is a storage layer used within the Databricks Lakehouse Platform that provides ACID transactions, schema enforcement, time travel, and improved reliability for large-scale data engineering workflows.

11. Is hands-on Databricks experience necessary before taking the exam?

Yes. Practical experience working with Databricks notebooks, Spark transformations, Delta tables, and data pipelines is highly recommended because many exam questions are scenario-oriented.

12. What job roles can benefit from this certification?

This certification is valuable for roles such as:

• Data Engineer

• Cloud Data Engineer

• Analytics Engineer

• Big Data Developer

• ETL Specialist

• Data Platform Engineer

• Spark Developer

13. Can beginners prepare for the Databricks Data Engineer Associate certification?

Yes, but beginners should first build foundational knowledge in data engineering, SQL, Apache Spark, and cloud-based analytics before attempting the certification exam.

14. Where can I register for the Databricks certification exam?

Candidates can register through the official Databricks certification portal and authorized exam delivery platform provided by Databricks.

15. Why is the Databricks Certified Data Engineer Associate certification valuable?

The certification demonstrates practical data engineering skills aligned with modern cloud analytics and lakehouse architectures. It helps professionals validate technical expertise and improve credibility in enterprise data engineering environments.