AWS Machine Learning Specialty Sample Questions & MLS-C01 Dumps

Prepare confidently for the AWS Machine Learning Specialty exam with a curated set of real-world exam questions and MLS-C01 practice dumps. This comprehensive guide includes scenario-based AWS certified machine learning specialty sample questions and domain-focused practice exams covering data engineering, modeling, algorithm selection, and ML implementation on AWS. Whether you're a data scientist, ML engineer, or aspiring cloud professional, these AWS Machine Learning Specialty exam questions will help validate your skills. With updated MLS-C01 dumps and mock exams, assess your readiness and enhance your chances of success in one of AWS’s most challenging certifications. Begin your journey to earning the AWS ML Certification with trusted preparation material designed for real exam environments.

AWS Machine Learning Specialty Sample Questions List :

1. A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset: Must be accessible from a VPC only. Must not traverse the public internet. How can these requirements be satisfied?

1. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

2. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

3. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

4. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

2. A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. Which machine learning model type should the Specialist use to accomplish this task?

1. Linear regression

2. Classification

3. Clustering

4. Reinforcement learning

3. A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively. How should the Specialist address this issue and what is the reason behind it?

1. The learning rate should be increased because the optimization process was trapped at a local minimum.

2. The dropout rate at the flatten layer should be increased because the model is not generalized enough.

3. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

4. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

4. An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

1. Add vanishing gradient to the model

2. Perform data augmentation on the training data

3. Make the neural network architecture complex.

4. Use gradient checking in the model

5. Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

1. Pre-split the data before uploading to Amazon S3

2. Have Amazon ML split the data randomly.

3. Have Amazon ML split the data sequentially.

4. Perform custom cross-validation on the data

6. Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

1. Recall

2. Misclassification rate

3. Mean absolute percentage error (MAPE)

4. Area Under the ROC Curve (AUC)

7. A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

1. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

2. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

3. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of regressor.

4. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of classifier.

8. A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team. Which solution requires the LEAST coding effort?

1. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

2. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

3. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

4. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

9. A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?

1. Use the SageMaker batch transform feature to transform the training data into a DataFrame

2. Use AWS Glue to compress the data into the Apache Parquet format

3. Transform the dataset into the Recordio protobuf format

4. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

10. A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet. How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

1. Create a NAT gateway within the corporate VPC.

2. Route Amazon SageMaker traffic through an on-premises network.

3. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

4. Create VPC peering with Amazon VPC hosting Amazon SageMaker.

11. A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

1. The model needs to be completely re-engineered because it is unable to handle product inventory changes

2. The model's hyperparameters should be periodically updated to prevent drift

3. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

4. The model should be periodically retrained using the original training data plus new data as product inventory changes

12. A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

1. Receiver operating characteristic (ROC) curve

2. Misclassification rate

3. Root Mean Square Error (RM&)

4. L1 norm

13. A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?

1. K-means clustering

2. Random Cut Forest (RCF)

3. XGBoost

4. BlazingText

14. A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

1. Perform one-hot encoding on highly correlated features

2. Use matrix multiplication on highly correlated features.

3. Create a new feature space using principal component analysis (PCA)

4. Apply the Pearson correlation coefficient

15. A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

1. Reduce image resolution and use reduced resolution pixel values as features

2. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

3. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

4. Average colors by channel to obtain three-dimensional representations of images.

16. A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

1. Use an Amazon SageMaker notebook for both feature engineering and model development

2. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

3. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

4. Use Amazon ML for both feature engineering and model development.

17. A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs. What does the Specialist need to do?

1. Bundle the NVIDIA drivers with the Docker image.

2. Build the Docker container to be NVIDIA-Docker compatible.

3. Organize the Docker container's file structure to execute on GPU instances.

4. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

18. A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

1. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

2. AWS Glue with a custom ETL script to transform the data.

3. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

4. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

19. An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

1. The factorization machines (FM) algorithm

2. The Latent Dirichlet Allocation (LDA) algorithm

3. The principal component analysis (PCA) algorithm

4. The k-means algorithm

20. A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?

1. A longer training time

2. Making the network larger

3. Using a different optimizer

4. Using some form of regularization

21. A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions Here is an example from the dataset "The quck BROWN FOX jumps over the lazy dog " Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

1. Perform part-of-speech tagging and keep the action verb and the nouns only

2. Normalize all words by making the sentence lowercase

3. Remove stop words using an English stopword dictionary.

4. Correct the typography on "quck" to "quick."

22. A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture. Which of the following will accomplish this? (Select TWO.)

1. Customize the built-in image classification algorithm to use Inception and use this for model training.

2. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

3. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

4. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.

23. While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

1. Dimensionality reduction

2. Data normalization

3. Model regulanzation

4. Data augmentation for the minority class

24. A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

1. AWSDMS

2. Amazon Kinesis Data Streams

3. Amazon Kinesis Data Firehose

4. Amazon Kinesis Data Analytics

25. A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?

1. A histogram showing whether the most important input feature is Gaussian.

2. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.

3. A scatter plot showing (he performance of the objective metric over each training iteration

4. A scatter plot showing the correlation between maximum tree depth and the objective metric.

FAQs

1. What is the AWS Certified Machine Learning Specialty MLS-C01 certification?

It is a specialty-level AWS certification that validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

2. How do I become AWS Certified Machine Learning Specialty certified?

You need to study ML and AWS AI/ML services, register for the MLS-C01 exam on the AWS Certification Portal, and pass it.

3. What are the prerequisites for the AWS Certified Machine Learning Specialty exam?

There are no mandatory prerequisites, but AWS recommends at least 1–2 years of experience in ML or deep learning and knowledge of AWS cloud services.

4. How much does the AWS MLS-C01 certification exam cost?

The exam fee is $300 USD.

5. How many questions are on the AWS Certified Machine Learning Specialty exam?

The exam has 65 multiple-choice and multiple-response questions.

6. What is the passing score for the AWS Machine Learning Specialty MLS-C01 exam?

You need a scaled score of 750 out of 1000 to pass.

7. How long is the AWS Machine Learning Specialty certification exam?

The exam duration is 180 minutes.

8. What topics are covered in the AWS Certified Machine Learning Specialty exam?

It covers data engineering, exploratory data analysis, modeling, ML implementation, and ML operations on AWS.

9. How difficult is the AWS MLS-C01 certification exam?

It is considered challenging, requiring both ML expertise and AWS service knowledge.

10. How long does it take to prepare for the AWS Certified Machine Learning Specialty exam?

Most candidates prepare in 8–12 weeks, depending on prior ML and AWS experience.

11. Are there any AWS Certified Machine Learning Specialty sample questions or practice tests available?

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

12. What is the validity period of the AWS Certified Machine Learning Specialty certification?

The certification is valid for 3 years.

13. Can I retake the AWS MLS-C01 exam if I fail?

Yes, you can retake it after 14 days by paying the exam fee again.

14. What jobs can I get with an AWS Certified Machine Learning Specialty certification?

You can work as a Machine Learning Engineer, Data Scientist, AI Specialist, or Cloud ML Engineer.

15. How much salary can I earn with the AWS Certified Machine Learning Specialty MLS-C01 certification?

Certified professionals typically earn between $120,000–$160,000 annually, depending on experience and location.