Free Access to Amazon.MLA-C01.v2026-04-16.q120 with Valid Practice Test (Page 8)

Question 31

An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker AI.
Which solution will provide this functionality with the LEAST implementation effort?

A.Manually install the libraries in the SageMaker AI containers.
B.Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker AI jobs and pipelines.
C.Use a SageMaker AI notebook instance and install libraries at startup.
D.Run code externally on Amazon EC2 and import results into SageMaker AI.

Question 32

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?

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

Question 33

A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket.
Data transformation scripts and queries are now taking much longer than they used to take.
An ML engineer must implement a solution to optimize the data for query performance.
Which solution will meet this requirement with the LEAST operational overhead?

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

Question 34

An ML engineer is designing an AI-powered traffic management system. The system must use near real-time inference to predict congestion and prevent collisions.
The system must also use batch processing to perform historical analysis of predictions over several hours to improve the model. The inference endpoints must scale automatically to meet demand.
Which combination of solutions will meet these requirements? (Select TWO.)

A.Use Amazon SageMaker real-time inference endpoints with automatic scaling based on ConcurrentInvocationsPerInstance.
B.Use AWS Lambda with reserved concurrency and SnapStart to connect to SageMaker endpoints.
C.Use an Amazon SageMaker Processing job for batch historical analysis. Schedule the job with Amazon EventBridge.
D.Use Amazon EC2 Auto Scaling to host containers for batch analysis.
E.Use AWS Lambda for historical analysis.

Question 35

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.
Which AWS service or feature can aggregate the data from the various data sources?

A.Amazon EMR Spark jobs
B.Amazon Kinesis Data Streams
C.Amazon DynamoDB
D.AWS Lake Formation

Correct Answer: A

* Problem Description:
* The dataset includes multiple data sources:
* Transaction logs and customer profiles in Amazon S3.
* Tables in an on-premises MySQL database.
* There is a class imbalance in the dataset and interdependencies among features that need to be addressed.
* The solution requires data aggregation from diverse sources for centralized processing.
* Why AWS Lake Formation?
* AWS Lake Formation is designed to simplify the process of aggregating, cataloging, and securing data from various sources, including S3, relational databases, and other on-premises systems.
* It integrates with AWS Glue for data ingestion and ETL (Extract, Transform, Load) workflows, making it a robust choice for aggregating data from Amazon S3 and on-premises MySQL databases.
* How It Solves the Problem:
* Data Aggregation: Lake Formation collects data from diverse sources, such as S3 and MySQL, and consolidates it into a centralized data lake.
* Cataloging and Discovery: Automatically crawls and catalogs the data into a searchable catalog, which the ML engineer can query for analysis or modeling.
* Data Transformation: Prepares data using Glue jobs to handle preprocessing tasks such as addressing class imbalance (e.g., oversampling, undersampling) and handling interdependencies among features.
* Security and Governance: Offers fine-grained access control, ensuring secure and compliant data management.
* Steps to Implement Using AWS Lake Formation:
* Step 1: Set up Lake Formation and register data sources, including the S3 bucket and on-premises MySQL database.
* Step 2: Use AWS Glue to create ETL jobs to transform and prepare data for the ML pipeline.
* Step 3: Query and access the consolidated data lake using services such as Athena or SageMaker for further ML processing.
* Why Not Other Options?
* Amazon EMR Spark jobs: While EMR can process large-scale data, it is better suited for complex big data analytics tasks and does not inherently support data aggregation across sources like Lake Formation.
* Amazon Kinesis Data Streams: Kinesis is designed for real-time streaming data, not batch data aggregation across diverse sources.
* Amazon DynamoDB: DynamoDB is a NoSQL database and is not suitable for aggregating data from multiple sources like S3 and MySQL.
Conclusion: AWS Lake Formation is the most suitable service for aggregating data from S3 and on-premises MySQL databases, preparing the data for downstream ML tasks, and addressing challenges like class imbalance and feature interdependencies.
AWS Lake Formation Documentation
AWS Glue for Data Preparation

Question 31

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Question 33

Question 34

Question 35

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