MLA-C01 Sagemaker Practice Questions

A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance. Which solution will meet these requirements in the LEAST amount of time?

• AA. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
• BB. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
• CC. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
• DD. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running. How should the company deploy the model on Amazon SageMaker to meet these requirements?

• AA. Use a multi-model serverless endpoint. Enable caching.
• BB. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
• CC. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
• DD. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score. During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly. What could be the reason for the reduced F1 score?

• AA. Concept drift occurred in the underlying customer data that was used for predictions.
• BB. The model was not sufficiently complex to capture all the patterns in the original baseline data.
• CC. The original baseline data had a data quality issue of missing values.
• DD. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.