Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 31:

  A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive

  different answers. An ML engineer needs to improve the responses to be more consistent and less random.

  Which solution will meet these requirements?

  A. Increase the temperature parameter and the top_k parameter.
  B. Increase the temperature parameter. Decrease the top_k parameter.
  C. Decrease the temperature parameter. Increase the top_k parameter.
  D. Decrease the temperature parameter and the top_k parameter.
  D. Decrease the temperature parameter and the top_k parameter.

• Question 32:

  A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models. The ML engineers need to generate

  daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.

  Which solution will provide the HIGHEST performance for data retrieval?

  A. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
  B. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
  C. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
  D. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
  C. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.

• Question 33:

  An ML engineer must ensure that all data is encrypted in transit during the execution of an ML training job. This includes applying encryption in transit to all processes utilized by Amazon SageMaker throughout the training job. Which solution will effectively fulfill these requirements?

  A. Encrypt communication between nodes for batch processing.
  B. Encrypt communication between nodes in a training cluster.
  C. Specify an AWS Key Management Service (AWS KMS) key during creation of the training job request.
  D. Specify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.
  B. Encrypt communication between nodes in a training cluster.

• Question 34:

  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. The

  training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

  Which action will meet this requirement with the LEAST operational overhead?

  A. Use AWS Glue to transform the categorical data into numerical data.
  B. Use AWS Glue to transform the numerical data into categorical data.
  C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
  C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

• Question 35:

  An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. During testing, the model excels at identifying fraud in the

  training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.

  What should the ML engineer do to improve the fraud detection for new transactions?

  A. Increase the learning rate.
  B. Remove some irrelevant features from the training dataset.
  C. Increase the value of the max_depth hyperparameter.
  D. Decrease the value of the max_depth hyperparameter.
  D. Decrease the value of the max_depth hyperparameter.

• Question 36:

  An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable. Which instance purchasing option will meet these requirements MOST cost-effectively?

  A. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  B. Run the primary node, core nodes, and task nodes on Spot Instances.
  C. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
  D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

• Question 37:

  An ML engineer is using a training job to ne-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and over tting problems. The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics

  during the training.

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

  A. Use TensorBoard to monitor the training job. Publish the ndings to an Amazon Simple notfication Service (Amazon SNS) topic. Create an AWS Lambda function to consume the ndings and to initiate the predefined actions.
  B. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  C. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.
  D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

• Question 38:

  Which AWS service can help label large datasets efficiently using active learning and human annotators?

  A. Amazon Rekognition
  B. AWS Lambda
  C. Amazon SageMaker Ground Truth
  D. Amazon Comprehend
  C. Amazon SageMaker Ground Truth

• Question 39:

  An IoT company leverages Amazon SageMaker to train and test an XGBoost model for object detection. The ML engineers need to monitor performance metrics during training with different hyperparameter variations. Additionally, they require a solution to send SMS text messages once the training is complete.

  Which solution would effectively address these requirements?

  A. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
  B. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.
  C. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
  D. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.
  B. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.

• Question 40:

  A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is Configured properly, and ML engineers can access the SageMaker 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 con guration will meet this requirement?

  A. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  B. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
  C. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
  D. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
  B. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.