MLA-C01 practice questions 151–160

1. Question 151 · 226 in bank

   A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents. Which solution will meet this requirement with the LEAST operational effort?

   • AUse Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions.
   • BUse Amazon SageMaker AI to customize an AI model to monitor driver behavior and identify distractions.
   • CIntegrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions
   • DUse Amazon Comprehend to analyze text-based driver feedback and identify distractions.

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   Answer: A

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2. Question 152 · 226 in bank

   A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes. The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python. Which solution will meet these requirements?

   • AUse AWS CloudFormation to provision Amazon EC2 instances in multiple Availability Zones. Set up a Kubernetes cluster. Host the model container on the Kubernetes cluster.
   • BUse the AWS CLI to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
   • CUse the AWS Cloud Development Kit (AWS CDK) to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
   • DUse AWS CloudFormation to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.

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   Answer: C

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3. Question 153 · 226 in bank

   An ML engineer is developing a linear regression ML model. The model shows high accuracy on the training dataset but performs poorly on unseen new data. Which action should the ML engineer take to address this issue?

   • AIncrease the complexity of the model to capture more patterns in the training data. Use Amazon SageMaker Debugger to monitor for convergence issues.
   • BApply ML techniques such as cross-validation and regularization. Use Amazon SageMaker Experiments to track and compare different model versions and their performance metrics.
   • CDirectly deploy the model into production. Use Amazon SageMaker Clarify to interpret model outputs on new data. Adjust the model based on these insights.
   • DIncrease the size of the training dataset without adjusting the size of the model. Retrain the model on the new data. Generate a confusion matrix to analyze the results.

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   Answer: B

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4. Question 154 · 226 in bank

   A company is training a new ML model to replace a model that is deployed on an Amazon SageMaker AI real-time endpoint. An ML engineer needs to determine the latency and the accuracy of the new model. The ML engineer must evaluate the new model in a production scenario without affecting the users of the existing model. Which solution will meet these requirements?

   • APerform a blue/green deployment with linear traffic shifting.
   • BPerform a blue/green deployment with canary traffic shifting.
   • CPerform a rolling deployment with a rolling batch size of 50% of the current fleet.
   • DPerform shadow testing with a traffic sampling percentage of 100%.

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   Answer: D

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5. Question 155 · 226 in bank

   An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata. Which solution will meet these requirements with the LEAST manual effort?

   • AUse AWS Glue to run the ETL job. Use the job to discover the schema and to store the associated metadata in the AWS Glue Data Catalog.
   • BCreate an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the schema and to store the associated metadata in an S3 bucket.
   • CCreate an ETL pipeline by using Amazon Athena integrated with AWs Step Functions. Use the pipeline to run the ETL job to discover the schema and to store the associated metadata in an S3 bucket.
   • DLaunch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the job to discover the schema and to store the associated metadata in Amazon Redshift.

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   Answer: A

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6. Question 156 · 226 in bank

   An ML engineer is building an ML pipeline. The pipeline must process a dataset in two ways by using Amazon Athena. The pipeline must use batch processing to perform large-scale data transformations and for model training. The pipeline must also use near real-time processing to perform low-latency queries for inference and analytics. Which file format will provide the LEAST latency for both types of processing?

   • ACSV
   • BApache Parquet
   • CNested JSON
   • DDeserialized JSON

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   Answer: B

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7. Question 157 · 226 in bank

   A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inferences. During times of peak usage, new instances launch before existing instances are fully ready. As a result, the model experiences inefficiencies and delays. Which solution will optimize the scaling process without affecting response times?

   • AChange to a multi-model endpoint configuration in SageMaker AI.
   • BIntegrate Amazon API Gateway and AWS Lambda to manage invocations of the SageMaker AI inference endpoint.
   • CDecrease the cooldown period for scale-in activities. Increase the maximum number of instances.
   • DIncrease the cooldown period after scale-out activities.

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   Answer: D

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8. Question 158 · 226 in bank

   A company deployed an Amazon SageMaker AI ML model to an endpoint by calling the CreateModel API operation. The network that was established with the API call includes two private subnets and one security group. The model must download data from an Amazon S3 bucket and must upload data to the S3 bucket. The traffic to the S3 bucket must not travel across the internet. Which solution will meet these requirements?

   • ACreate a NAT gateway. Configure the security group to allow outbound connections. Configure route tables to redirect any traffic to the S3 bucket through the NAT gateway.
   • BCreate a gateway VPC endpoint. Configure an endpoint policy that restricts access to the S3 bucket. Configure route tables to redirect any traffic to the S3 bucket through the endpoint.
   • CCreate an interface VPC endpoint. Verify that the security group allows only inbound connections. Configure route tables to redirect any traffic to the S3 bucket through the endpoint.
   • DCreate a Gateway Load Balancer VPC endpoint. Configure an IAM policy that restricts access to the S3 bucket. Configure route tables to redirect any traffic to the S3 bucket through the endpoint.

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   Answer: B

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9. Question 159 · 226 in bank

   A company is developing a new ML model to rank customers in order of their potential to pay back loans. The company needs to use an Amazon SageMaker AI built-in algorithm. Which algorithm should the company use to meet these requirements?

   • AXGBoost
   • BK-means clustering
   • CPrincipal component analysis (PCA)
   • DNeural Topic Model (NTM)

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   Answer: A

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10. Question 160 · 226 in bank

    An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected. How should the ML engineer set up the pipeline to meet this requirement?

    • AUse an AWS Glue crawler and an AWS Glue extract, transform and load (ETL) job to detect data drift. Use AWS Glue triggers to automate the re-training job.
    • BUse Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda function to automate the re-training job.
    • CUse SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
    • DUse Amazon QuickSight anomaly detection to detect data drift. Use an AWS Step Functions workflow to automate the re-training job.

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    Answer: C

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MLA-C01 practice, question 151. 226 questions available.