Amazon MLS-C01 Free Sample Exam Questions 2026

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Page: 1 / 62
Total Questions: 307

Questions & Answers PDF

• Question 1

  

  A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.Which solution will meet these requirements with the LEAST development effort?

  • Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
  • Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.
  • Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
  • Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

  Reveal Answer Answer: D Next Question

• Question 2

  

  A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

  • Amazon SageMaker seq2seq algorithm
  • Amazon SageMaker BlazingText algorithm in Skip-gram mode
  • Amazon SageMaker Object2Vec algorithm
  • Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
  • Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

  Reveal Answer Answer: B, E Next Question

• Question 3

  

  A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns onlyHow should the Machine Learning Specialist transform the dataset to minimize query runtime?

  • Convert the records to Apache Parquet format
  • Convert the records to JSON format
  • Convert the records to GZIP CSV format
  • Convert the records to XML format

  Reveal Answer Answer: A Next Question

• Question 4

  

  A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

  • Root Mean Square Error (RMSE)
  • Residual plots
  • Area under the curve
  • Confusion matrix

  Reveal Answer Answer: B Next Question

• Question 5

  

  A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpointWhich combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

  • Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
  • Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
  • Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects
  • Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
  • Include the SQL statement 'USING EXTERNAL FUNCTION ml_function_name' in the Athena SQL query.
  • Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.

  Reveal Answer Answer: B, C, E Next Question

• Question 6

  

  An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.Which solution will improve the computational efficiency of the models?

  • Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
  • Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
  • Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
  • Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.

  Reveal Answer Answer: C Next Question

• Question 7

  

  A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

  • Use Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'
  • Use a forecasting algorithm to run predictions.
  • Use a regression algorithm to run predictions.
  • Use a classification algorithm to run predictions
  • Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

  Reveal Answer Answer: A, D Next Question

• Question 8

  

  A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  • Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
  • Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
  • Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

  Reveal Answer Answer: D Next Question

• Question 9

  

  A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

  • AWS CloudTrail
  • AWS Health
  • AWS Trusted Advisor
  • Amazon CloudWatch
  • AWS Config

  Reveal Answer Answer: A, D Next Question

• Question 10

  

  A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

  • Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  • Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

  Reveal Answer Answer: B Next Question

Page: 1 / 62
Total Questions: 307

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