MLA-C01 practice questions 161–170

1. Question 161 · 226 in bank

   A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker AI. The company has not hosted a model on SageMaker AI previously. An ML engineer needs to implement a solution to track model versions. The solution also must provide recommendations about which Amazon EC2 instance types to use to host the model. Which solution will meet these requirements?

   • ARegister the model in Amazon Elastic Container Registry (Amazon ECR). Use AWS Compute Optimizer for recommendations about instance types.
   • BRegister the model in the SageMaker Model Registry. Use SageMaker Autopilot for recommendations about instance types.
   • CRegister the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types.
   • DRegister the model in Amazon Elastic Container Registry (Amazon ECR). Use SageMaker Experiments for recommendations about instance types.

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   Answer: C

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2. Question 162 · 226 in bank

   A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment. Which solution will meet this requirement?

   • AAdd more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
   • BImplement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
   • CUse principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers. Implement cross-entropy loss functions.
   • DAugment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.

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   Answer: B

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3. Question 163 · 226 in bank

   A company collects customer data every day. The company stores the data as compressed files in an Amazon S3 bucket that is partitioned by date. Every month, analysts download the data, process the data to check the data quality, and then upload the data to Amazon QuickSight dashboards. An ML engineer needs to implement a solution to automatically check the data quality before the data is sent to QuickSight. Which solution will meet these requirements with the LEAST operational overhead?

   • ARun an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality.
   • BUse an AWS Glue trigger to run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Create an AWS Glue job that loads the data into a PySpark DataFrame. Configure the job to apply custom functions and to evaluate the data quality.
   • CRun Python scripts on an AWS Lambda function every month to evaluate data quality. Configure the S3 bucket to invoke the Lambda function when objects are added to the S3 bucket.
   • DConfigure the S3 bucket to send event notifications to an Amazon Simple Queue Service (Amazon SQS) queue when objects are uploaded. Use Amazon CloudWatch insights every month for the SQS queue to evaluate the data quality.

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   Answer: A

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4. Question 164 · 226 in bank

   A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features. Which solution will meet this requirement with the LEAST operational overhead?

   • AConfigure SageMaker Model Monitor. Establish a data quality baseline. Ensure that the emit_metrics option is enabled in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in specific metrics that are related to data quality.
   • BConfigure SageMaker Model Monitor. Establish a model quality baseline. Ensure that the comparison_method option is set to Robust in the baseline constraints file. Configure an Amazon CloudWatch alarm to notify the company about changes in model quality metrics.
   • CUse SageMaker Debugger with custom rules to track shifts in feature distributions. Configure Amazon CloudWatch alarms to notify the company when the rules detect significant changes.
   • DUse Amazon CloudWatch to directly observe the SageMaker AI endpoint’s performance metrics. Manually analyze the CloudWatch logs for indicators of data drift or shifts in feature distribution.

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   Answer: A

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5. Question 165 · 226 in bank

   A company is using Amazon SageMaker AI to deploy a new recommendation model for its ecommerce website. The model must use data from all client website interactions as input. Traffic is variable throughout the day. The company needs to create an inference endpoint for the model. Which type of inference endpoint will meet these requirements MOST cost-effectively?

   • ABatch transform inference endpoint
   • BAsynchronous inference endpoint
   • CReal-time inference endpoint
   • DServerless inference endpoint

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   Answer: D

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6. Question 166 · 226 in bank

   A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain. Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address. Which update to the network configuration will meet this requirement?

   • ACreate a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
   • BCreate a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.
   • CCreate a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
   • DCreate a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

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   Answer: B

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7. Question 167 · 226 in bank

   A company’s ML engineer is creating a classification model. The ML engineer explores the dataset and notices a column that is named day_of_week. The column’s data consists of the following values: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday. Which technique should the ML engineer use to convert this column’s data to binary values?

   • ABinary encoding
   • BLabel encoding
   • COne-hot encoding
   • DTokenization

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   Answer: C

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8. Question 168 · 226 in bank

   An ML engineer wants to use Amazon SageMaker AI to prepare data for training. During exploratory data analysis, the ML engineer notices that several categorical features are missing values. How can the ML engineer use SageMaker AI to solve this problem?

   • AUse SageMaker Clarify to impute categorical features with the mean value.
   • BUse SageMaker Clarity to impute categorical features with the mode value.
   • CUse SageMaker Data Wrangler to impute categorical features with the mean value.
   • DUse SageMaker Data Wrangler to impute categorical features with the mode value.

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   Answer: D

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9. Question 169 · 226 in bank

   A company stores user clickstream data in an Amazon S3 bucket in AWS Account A. The company needs to use the data to train an ML model in Amazon SageMaker AI in AWS Account B. The training will take 10 days. The company needs to use only private IP addresses in the training. The company also must make sure that no training metadata is shared with AWS. Which solution will meet these requirements?

   • ASet up VPC peering between Account A and Account B. Contact AWS by email to opt out of metadata collection.
   • BSet up a VPC endpoint for the S3 bucket. Set the SageMaker AI OPT_OUT_TRACKING environment variable to 1 in the training job.
   • CConfigure a security group policy that is assigned to the S3 bucket in Account A to allow access from only Account B. Create AI services opt-out policies.
   • DGenerate presigned URLs with expiration times for the objects that are stored in the S3 bucket. Access the data by using the presigned URLs. Set the SageMaker AI OPT_OUT_TRACKING environment variable to 1 in the training job.

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   Answer: B

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10. Question 170 · 226 in bank

    A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket. The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model. The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference. Which solution will meet these requirements?

    • AIngest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.
    • BUse Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
    • CUse AWS Lake Formation. Apply tag-based control on the data.
    • DUse the Generate Data Insights function in SageMaker Data Wrangler.

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    Answer: A

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MLA-C01 practice, question 161. 226 questions available.