Free MLA-C02 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question

Answer is in the explanation below.

Answer is in the explanation below.

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• ○ Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
• ○ Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
• ○ Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
• ○ Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.

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• ○ Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
• ○ Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
• ○ Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
• ○ Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.

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• ○ AWS::SageMaker::Model
• ○ AWS::SageMaker::Endpoint
• ○ AWS::SageMaker::NotebookInstance
• ○ AWS::SageMaker::Pipeline

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• ○ Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
• ○ Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
• ○ Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
• ○ Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

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• ○ Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
• ○ Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
• ○ Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
• ○ Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.

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• ○ Adjust the model's parameters and hyperparameters.
• ○ Initiate a manual Model Monitor job that uses the most recent production data.
• ○ Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
• ○ Include additional data in the existing training set for the model. Retrain and redeploy the model.

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• ○ Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
• ○ Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
• ○ Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
• ○ Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

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• ○ Enable S3 bucket versioning.
• ○ Configure S3 Object Lock settings for each user.
• ○ Add cross-origin resource sharing (CORS) policies to the S3 buckets.
• ○ Create IAM policies. Attach the policies to IAM users or IAM roles.

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• ○ Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
• ○ Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
• ○ Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
• ○ Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

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• ○ Use Amazon Made to categorize the sensitive data.
• ○ Prepare the data by using AWS Glue DataBrew.
• ○ Run an AWS Batch job to change the sensitive data to random values.
• ○ Run an Amazon EMR job to change the sensitive data to random values.

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• ○ Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
• ○ Use DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
• ○ Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.
• ○ Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in AWS Glue Parquet format.

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

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• ○ Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
• ○ Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
• ○ Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
• ○ Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.

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• ○ Deploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
• ○ Deploy the model to an Amazon SageMaker real-time endpoint.
• ○ Deploy the model to an Amazon SageMaker Asynchronous Inference endpoint.
• ○ Package the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.

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• ○ Binary encoding
• ○ Label encoding
• ○ One-hot encoding
• ○ Statistical imputation
• ○ Tokenization

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• ○ Configure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.
• ○ Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.
• ○ Configure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache Parquet format.
• ○ Configure an Amazon EMR cluster to process the data that is in the S3 bucket.

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• ○ Create a multi-container inference endpoint for direct invocation.
• ○ Create a multi-model inference endpoint for all the models.
• ○ Create a multi-container inference endpoint for sequential invocation.
• ○ Create multiple single-model inference endpoint for each model.

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• ○ Run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality.
• ○ Use 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.
• ○ Run 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.
• ○ Configure 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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• ○ Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
• ○ Create 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.
• ○ Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
• ○ Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

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• ○ Apply tokenization to location. Apply ordinal encoding to job_seniority_level.
• ○ Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level
• ○ Apply binning to location. Apply standard scaling to job_seniority_level.
• ○ Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.

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• ○ Use the latest version of Amazon Titan Text Premier to perform document embedding.
• ○ Use the latest version of Mistral 7B Instruct to perform instruction tuning.
• ○ Use the latest version of Anthropic Claude Sonnet to perform instruction tuning.
• ○ Use Cohere Embed Multilingual to perform document embedding.

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• ○ Use Amazon Rekognition and AWS Lambda to extract and analyze the metadata from the videos’ image frames.
• ○ Use Amazon Rekognition and a large language model (LLM) hosted on Amazon Bedrock to extract and analyze the metadata from the videos’ image frames.
• ○ Use Amazon SageMaker AI to extract and analyze the metadata from the videos’ image frames.
• ○ Use Amazon Transcribe and Amazon Comprehend to extract and analyze the metadata from the videos’ image frames.