Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 81:

  An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific

  number of epochs.

  Which solutions will mitigate this problem? (Choose two.)

  A. Enable early stopping on the model.
  B. Increase dropout in the layers.
  C. Increase the number of layers.
  D. Increase the number of neurons.
  E. Investigate and reduce the sources of model bias.
  A. Enable early stopping on the model.
  B. Increase dropout in the layers.

• Question 82:

  An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day. The ML engineer needs a scalable solution that minimizes costs when the model is not in

  use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.

  Which solution will meet these requirements?

  A. Create AWS Lambda functions that have xed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
  C. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route tra ffic between the different copies of the model at the endpoint.
  D. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
  D. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

• Question 83:

  An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a

  transaction date. The ML engineer must query the data based on the transaction date.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.
  B. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
  C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.
  D. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
  A. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.

• Question 84:

  In Amazon SageMaker, which of the following is a managed capability for hyperparameter tuning?

  A. Batch transform
  B. AutoPilot
  C. Ground Truth
  D. Hyperparameter Tuning Jobs
  D. Hyperparameter Tuning Jobs

• Question 85:

  An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these requirements?

  A. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  C. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.
  B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

• Question 86:

  An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in

  Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. After

  the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

  Which solution will meet these requirements?

  A. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
  B. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  C. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
  D. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  C. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

• Question 87:

  A company is using ML to predict the presence of a specific weed in a farmer's eld. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter. What should the company do to MINIMIZE false positives?

  A. Set the value of the weight decay hyperparameter to zero.
  B. Increase the number of training epochs.
  C. Increase the value of the target_precision hyperparameter.
  D. Change the value of the predictorjype hyperparameter to regressor.
  C. Increase the value of the target_precision hyperparameter.

• Question 88:

  Which AWS service is best suited for building, training, and deploying machine learning models quickly?

  A. Amazon SageMaker
  B. AWS Lambda
  C. Amazon Redshift
  D. AWS Glue
  A. Amazon SageMaker

• Question 89:

  A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the

  inference results. An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notfication when a deviation in model quality occurs.

  Which solution will meet these requirements?

  A. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notfications about model quality.
  B. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notfications about model quality.
  C. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notfications about model quality.
  D. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notfications about model quality.
  A. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notfications about model quality.

• Question 90:

  A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon SageMaker pipeline to

  retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.

  Which solution will meet these requirements with the LEAST operational effort?

  A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
  B. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
  C. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.
  D. Use Amazon Managed workflows for Apache Air ow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
  C. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.