Amazon MLA-C01 Exam Practice Test Instant Access

Question 1

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.

The model is using sensitive dat

a. An ML engineer needs to implement a solution to identify and remove the sensitive data.

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

ADeploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.

BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.

CUse Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

DUse Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Answer : C

Question 2

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

Which solution will meet these requirements?

AUse an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.

BUse a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.

CUse the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.

DUse an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

Answer : C

Question 3

A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.

Which solution will meet this requirement?

AStore the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.

BStore the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.

CStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.

DStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.

Answer : A

Question 4

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.

Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

AUse Amazon Mechanical Turk jobs to detect duplicates.

BUse Amazon QuickSight ML Insights to build a custom deduplication model.

CUse Amazon SageMaker Data Wrangler to pre-process and detect duplicates.

DUse the AWS Glue FindMatches transform to detect duplicates.

Answer : D

Question 5

A company is using ML to predict the presence of a specific weed in a farmer's field. 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?

ASet the value of the weight decay hyperparameter to zero.

BIncrease the number of training epochs.

CIncrease the value of the target_precision hyperparameter.

DChange the value of the predictorjype hyperparameter to regressor.

Answer : C

Question 6

Case study

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 dat

a. 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?

AUse AWS Glue to transform the categorical data into numerical data.

BUse AWS Glue to transform the numerical data into categorical data.

CUse Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

DUse Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Answer : C

Question 7

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 notification when a deviation in model quality occurs.

Which solution will meet these requirements?

AUse SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

BUse SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.

CUse SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

DKeep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

Answer : A

Amazon MLA-C01 AWS Certified Machine Learning Engineer - Associate Exam Practice Test