MLA-C01 practice questions 181–190

1. Question 181 · 226 in bank

   A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic. Which solution will meet these requirements?

   • AConfigure auto scaling on the SageMaker AI endpoint.
   • BCreate a new SageMaker AI endpoint. Deploy the model to the new endpoint.
   • CUse SageMaker Neo to optimize the model for inference.
   • DAttach an Auto Scaling group to the SageMaker AI endpoint.

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   Answer: A

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2. Question 182 · 226 in bank

   An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features. Which solution will meet these requirements?

   • AIncrease the number of training iterations. Retrain the model.
   • BApply L1 regularization to the training data. Retrain the model.
   • CDecrease the number of training iterations. Retrain the model.
   • DUse SageMaker Debugger to apply L1 regularization to the running model.

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   Answer: B

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3. Question 183 · 226 in bank

   An ML engineer wants to use Amazon SageMaker Data Wrangler to perform preprocessing on a dataset. The ML engineer wants to use the processed dataset to train a classification model. During preprocessing, the ML engineer notices that a text feature has a range of thousands of values that differ only by spelling errors. The ML engineer needs to apply an encoding method so that after preprocessing is complete, the text feature can be used to train the model. Which solution will meet these requirements?

   • APerform ordinal encoding to represent categories of the feature.
   • BPerform similarity encoding to represent categories of the feature.
   • CPerform one-hot encoding to represent categories of the feature.
   • DPerform target encoding to represent categories of the feature.

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   Answer: B

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4. Question 184 · 226 in bank

   An ML engineer is training a text generation model on Amazon SageMaker AI. After several epochs, the loss function does not converge, and the model’s accuracy on the validation dataset starts to show oscillating results. The ML engineer needs to ensure that the model achieves generalization. Which solution will meet this requirement?

   • AIncrease the learning rate and decrease the mini-batch size.
   • BIncrease the learning rate as the number of epochs increases.
   • CDecrease the learning rate and increase the mini-batch size.
   • DDecrease the learning rate and decrease the mini-batch size.

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   Answer: C

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5. Question 185 · 226 in bank

   A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store. The company needs a hybrid system to make the shared data store accessible to on-premises servers and Amazon SageMaker AI notebooks that will consume the data. File locking is required for the data producers. Which AWS storage solution will meet these requirements?

   • AUse an Amazon S3 bucket to store the data. Use Mountpoint for Amazon S3 to mount the S3 bucket to the on-premises servers and the SageMaker AI notebooks.
   • BUse an Amazon Elastic File System (Amazon EFS) file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks.
   • CUse an Amazon FSx for Lustre file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks.
   • DUse an Amazon Elastic Block Store (Amazon EBS) volume to store the data. Mount the volume to the on-premises servers and the SageMaker AI notebooks.

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   Answer: B

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6. Question 186 · 226 in bank

   A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns. The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training. Which solution will meet these requirements?

   • AConfigure an AWS Glue DataBrew project that connects to the data. Use the DataBrew interactive interface to create a recipe that performs the one-hot encoding transformation. Create a job to apply the transformation and to write the output back to an S3 bucket.
   • BConfigure an AWS Glue Data Catalog table that points to the data. Use Amazon Athena to write SQL commands to perform the one-hot encoding transformation. Configure Athena to write the query results back to an S3 bucket.
   • CConfigure an AWS Glue Data Catalog table that points to the data. Create an AWS Glue ETL interactive notebook. Use the notebook to perform the one-hot encoding transformation. Run the configured cells and write the results back to an S3 bucket.
   • DConfigure an Amazon Redshift cluster to access the data by using Redshift Spectrum. Use SQL commands to perform the one-hot encoding transformation within Amazon Redshift. Configure Amazon Redshift to write the results back to an S3 bucket.

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   Answer: A

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7. Question 187 · 226 in bank

   A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible on AWS. Which feature of SageMaker AI should the company use to meet these requirements?

   • ASageMaker AI built-in algorithms
   • BSageMaker Canvas
   • CSageMaker JumpStart
   • DSageMaker AI script mode

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   Answer: D

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8. Question 188 · 226 in bank

   A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wants to determine which prices are unusually high outliers and to display the outliers visually. Which solution will meet these requirements?

   • AUse a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to take the square roots of the outlier prices to generate the chart. Configure a custom AWS Lambda function to scan the data for anomalies.
   • BUse AWS Glue DataBrew to preprocess the data. Set the REMOVE_OUTLIERS operation to eliminate data rows that include unusually high prices. Invoke an AWS Lambda function to store the removed rows in Amazon DynamoDB.
   • CUse a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to square the outlier prices to generate the chart. Use QuickSight anomaly detection insights to determine which prices are unusually high.
   • DUse a QuickSight filter to find the lowest 10 values for sneaker price. Assign a specific color to the 10 lowest values.

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   Answer: C

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9. Question 189 · 226 in bank

   An ML engineer is using Amazon QuickSight anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity parameter to Low and above. The ML engineer sets the Direction parameter to All. What effect will the ML engineer observe in the anomaly detection results if the ML engineer changes the Direction parameter to Lower than expected?

   • AIncreased anomaly identification frequency and increased recall
   • BDecreased anomaly identification frequency and decreased recall
   • CIncreased anomaly identification frequency and decreased recall
   • DDecreased anomaly identification frequency and increased recall

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   Answer: B

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10. Question 190 · 226 in bank

    A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image. The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Which solution will meet this requirement with the LEAST operational overhead?

    • ARedesign the ML services to be configured in Kubeflow. Deploy the new Kubeflow managed ML services to the EKS cluster.
    • BUpload the Docker images to an Amazon Elastic Container Registry (Amazon ECR) repository. Configure a deployment pipeline to deploy the images to the EKS cluster.
    • CMigrate the training data to an Amazon Redshift cluster. Retrain the models from the migrated training data by using Amazon Redshift ML. Deploy the retrained models to the EKS cluster.
    • DConfigure an Amazon SageMaker AI notebook. Retrain the models with the same code. Deploy the retrained models to the EKS cluster.

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    Answer: B

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MLA-C01 practice, question 181. 226 questions available.