MLA-C01 practice questions 71–80

1. Question 71 · 226 in bank

   A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

   • AHyperband
   • BGrid search
   • CBayesian optimization
   • DRandom search

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   Answer: A

   Explanation

   A. Hyperband: Efficient & best --> Right answer B. Grid Search: Exhaustive and tries all combos C. Bayesian Optimization: Smart with best combination D. Random Search: Random

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

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2. Question 72 · 226 in bank

   A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses. Which solution will meet these requirements?

   • AProvision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
   • BProvision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
   • CProvision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
   • DProvision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.

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   Answer: D

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3. Question 73 · 226 in bank

   A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold. Which solution will meet this requirement?

   • ALog the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
   • BLog the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
   • CLog the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
   • DLog the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.

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   Answer: C

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4. Question 74 · 226 in bank

   A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks. What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

   • AAdjust the model's parameters and hyperparameters.
   • BInitiate a manual Model Monitor job that uses the most recent production data.
   • CCreate a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
   • DInclude additional data in the existing training set for the model. Retrain and redeploy the model.

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   Answer: C

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5. Question 75 · 226 in bank

   A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size. An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand. Which solution will meet these requirements with the LEAST operational overhead?

   • ACreate an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
   • BCreate an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
   • CCreate an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.
   • DCreate an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster. Specify a list of images to process for each AWS Batch job.

   Show answer

   Answer: B

   References

   • AWS documentation

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6. Question 76 · 226 in bank

   An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
   • BUse Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
   • CStore the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
   • DUse Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

   Show answer

   Answer: D

   References

   • AWS documentation

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7. Question 77 · 226 in bank

   A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups. The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker. Which solution will provide the ML engineers with the appropriate access?

   • AEnable S3 bucket versioning.
   • BConfigure S3 Object Lock settings for each user.
   • CAdd cross-origin resource sharing (CORS) policies to the S3 buckets.
   • DCreate IAM policies. Attach the policies to IAM users or IAM roles.

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   Answer: D

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8. Question 78 · 226 in bank

   HOTSPOT - A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket. Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.) • An S3 event notification invokes the pipeline when new data is uploaded. • S3 Lifecycle rule invokes the pipeline when new data is uploaded. • SageMaker retrains the model by using the data in the S3 bucket. • The pipeline deploys the model to a SageMaker endpoint. • The pipeline deploys the model to SageMaker Model Registry.

   

   This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

   Show the answer figure

   Answer: See figure below

   Explanation

   1. An S3 event notification invokes the pipeline when new data is uploaded. 2. SageMaker retrains the model by using the data in the S3 bucket. 3. The pipeline deploys the model to a SageMaker endpoint.

   Source: Adapted from community discussion — djeong95

   

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9. Question 79 · 226 in bank

   A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day. Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities. Which solution will meet these requirements?

   • ASchedule an Amazon SageMaker batch transform job by using AWS Lambda.
   • BConfigure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
   • CUse Amazon SageMaker Serverless Inference with provisioned concurrency.
   • DRun the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

   Show answer

   Answer: C

   Explanation

   Load is predictable and sustainable with 2 hrs usage pattern; Needs quick response as well; Sagemaker - Provisioned concurrency + Serverless inference will be able to support it. https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

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10. Question 80 · 226 in bank

    A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions. Which solution will provide an explanation for the model's predictions?

    • AUse SageMaker Model Monitor on the deployed model.
    • BUse SageMaker Clarify on the deployed model.
    • CShow the distribution of inferences from A/В testing in Amazon CloudWatch.
    • DAdd a shadow endpoint. Analyze prediction differences on samples.

    Show answer

    Answer: B

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MLA-C01 practice, question 71. 226 questions available.