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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.
Topic 2	Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 3	ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 4	Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q51-Q56):

NEW QUESTION # 51
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

A. Set up shadow testing with a shadow variant of the new model.
B. Set up blue/green deployments with canary traffic shifting.
C. Set up SageMaker Debugger and create a custom rule.
D. Set up blue/green deployments with all-at-once traffic shifting.

Answer: A

NEW QUESTION # 52
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket.
The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model.
The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference.
Which solution will meet these requirements?

A. Use Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
B. Use the Generate Data Insights function in SageMaker Data Wrangler.
C. Use AWS Lake Formation. Apply tag-based control on the data.
D. Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.

Answer: D

NEW QUESTION # 53
A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI.
Users upload high-resolution images to the tool.
The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete.
Which solution will meet these requirements?

A. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.
B. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
C. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.
D. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.

Answer: A

Explanation:
This use case involves large payloads (high-resolution images), variable processing time, and a requirement to notify users upon completion. AWS documentation recommends Amazon SageMaker asynchronous inference for exactly this scenario.
Asynchronous inference allows clients to submit inference requests without waiting for a response. The request payload is stored in Amazon S3, and the inference result is written back to S3 when processing is complete. This approach is designed for workloads that cannot meet real-time latency requirements and where payload sizes exceed real-time limits.
SageMaker asynchronous inference integrates natively with Amazon SNS to send notifications when inference completes or fails, which directly satisfies the user notification requirement.
Batch transform jobs are intended for offline, scheduled processing of large datasets and do not provide built- in user notification mechanisms. Amazon EFS is not required because SageMaker natively integrates with S3 for inference input and output.
AWS explicitly documents asynchronous inference as the preferred solution for large image inference with completion notifications.
Therefore, Option B is the correct and AWS-verified answer.

NEW QUESTION # 54
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key.
Which solution will meet these requirements?

A. Create a new SSH access key and use the AWS Encryption CLI to encrypt the file.
B. Create a new API key by using Amazon API Gateway and use it to encrypt the file.
C. Create a new IAM role with permissions for kms:GenerateDataKey and use the role to encrypt the file.
D. Create a new AWS Key Management Service (AWS KMS) key and use the AWS Encryption CLI with the KMS key to encrypt the file.

Answer: D

Explanation:
AWS Key Management Service (AWS KMS) is the recommended service for encryption and key access control. By creating a customer-managed KMS key, the company can define granular IAM policies that control which applications and roles can use the key.
The AWS Encryption CLI integrates directly with KMS and enables client-side encryption of files before storing them in Amazon S3. This approach ensures data is encrypted at rest and that only authorized principals can decrypt it.
SSH keys and API keys are not designed for data encryption. IAM roles alone do not create or manage encryption keys-they only grant permissions.
AWS documentation explicitly states that KMS customer-managed keys provide centralized key management, auditing, and access control.
Therefore, Option D is the correct and AWS-aligned solution.

NEW QUESTION # 55
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

A. Configure an Amazon Q Business retriever to exclude the competitor's name.
B. Configure the competitor's name as a blocked phrase in Amazon Q Business.
C. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
D. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

Answer: B

Explanation:
Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

NEW QUESTION # 56
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