MLA-C01 practice questions 91–100

1. Question 91 · 226 in bank

   A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements. Which metric should the ML engineer use for the model recalibration?

   • AAccuracy
   • BPrecision
   • CRecall
   • DSpecificity

   Show answer

   Answer: A

   Explanation

   A. Accuracy: Correct choice; maximizes both true positives and true negatives. Formula: (TP + TN) / Total Predictions B. Precision: Focuses only on true positives, not negatives. Formula: TP / (TP + FP) C. Recall: Focuses on capturing all true positives, ignoring negatives. Formula: TP / (TP + FN) D. Specificity: Focuses only on true negatives, ignoring positives. Formula: TN / (TN + FP)

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

   Next question

2. Question 92 · 226 in bank

   A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements?

   • AUse AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
   • BUse AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
   • CUse SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
   • DUse SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

   Show answer

   Answer: C

   Explanation

   The correct answer is C. Options B and D suggest using SageMaker Experiments, which is good for tracking experiments but doesn't provide the comprehensive lineage tracking and governance features that ML Lineage Tracking offers. Running history of experiments: SageMaker ML Lineage Tracking provides a comprehensive way to track the lineage of ML workflows, including datasets, algorithms, hyperparameters, and models. This fulfills the requirement for keeping a running history of model discovery experiments. Model governance for auditing and compliance: ML Lineage Tracking also supports model governance by providing detailed information about each step in the ML process, which is crucial for auditing and compliance verifications.

   Source: Adapted from community discussion — khchan123

   References

   • AWS documentation
   • AWS documentation

   Next question

3. Question 93 · 226 in bank

   A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE-S3). The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes. After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors. What should the ML engineer do to resolve this problem?

   • AUpdate the IAM policy that is attached to the execution role for the training jobs. Include the s3:ListBucket and s3:GetObject permissions.
   • BUpdate the S3 bucket policy that is attached to the S3 bucket. Set the value of the aws:SecureTransport condition key to True.
   • CUpdate the IAM policy that is attached to the execution role for the training jobs. Include the kms:Encrypt and kms:Decrypt permissions.
   • DUpdate the IAM policy that is attached to the user that created the training jobs. Include the kms:CreateGrant permission.

   Show answer

   Answer: C

   Next question

4. Question 94 · 226 in bank

   A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts. An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources. Which solution will meet these requirements with the LEAST development effort?

   • ACreate code to evaluate each instance's memory and compute usage.
   • BAdd cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
   • CCheck AWS CloudTrail event history for the creation of the resources.
   • DRun AWS Compute Optimizer.

   Show answer

   Answer: D

   Next question

5. Question 95 · 226 in bank

   A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements?

   • AConfigure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
   • BCreate a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.
   • CUse the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
   • DUse an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

   Show answer

   Answer: C

   Explanation

   The question asks for a "central catalog" so I believe metadata, lineage tracking are also "included". ECR could not be the solution.

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation

   Next question

6. Question 96 · 226 in bank

   A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model. Which solution will set up the required online validation with the LEAST operational overhead?

   • AUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
   • BUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
   • CCreate a new SageMaker endpoint. Use production variants to add the new model to the new endpoint. Monitor the number of invocations by using Amazon CloudWatch.
   • DConfigure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.

   Show answer

   Answer: A

   Explanation

   { 'ProductionVariants': [ { 'VariantName': 'existing-model', 'ModelName': 'existing-model', 'InitialVariantWeight': 0.9 }, { 'VariantName': 'new-model', 'ModelName': 'new-model', 'InitialVariantWeight': 0.1 } ] }

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation

   Next question

7. Question 97 · 226 in bank

   A company deployed an ML model that uses the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data. An AWS Lambda function provides the predictions to the company's application. An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time. Which solution will meet these requirements?

   • AUse Amazon CloudWatch to create a dashboard that monitors real-time inference data and model predictions. Use the dashboard to detect drift.
   • BModify the Lambda function to calculate model drift by using real-time inference data and model predictions. Program the Lambda function to send alerts.
   • CSchedule a monitoring job in SageMaker Model Monitor. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.
   • DSchedule a monitoring job in SageMaker Debugger. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.

   Show answer

   Answer: C

   Next question

8. Question 98 · 226 in bank

   A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item. Which Amazon SageMaker algorithm will meet these requirements?

   • AImage classification
   • BXGBoost
   • CObject detection
   • DK-nearest neighbors (k-NN)

   Show answer

   Answer: C

   References

   • AWS documentation

   Next question

9. Question 99 · 226 in bank

   A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet. An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future. Which solution will meet these requirements?

   • AUse DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
   • BUse DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
   • CSeparate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.
   • DSeparate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in AWS Glue Parquet format.

   Show answer

   Answer: A

   Explanation

   Should be A. C is incorrect because it involve separating the data by file type, which is unnecessary since DataBrew can process various file types within the same folder.

   Source: Adapted from community discussion — chris_spencer

   Next question

10. Question 100 · 226 in bank

    A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line. Which metrics should the company use to evaluate the model's performance? (Choose two.)

    • APrecision and recall
    • BRoot mean square error (RMSE) and mean absolute percentage error (MAPE)
    • CAccuracy and F1 score
    • DBilingual Evaluation Understudy (BLEU) score
    • EPerplexity

    Show answer

    Answer: C

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

MLA-C01 practice, question 91. 226 questions available.