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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 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 (Q184-Q189):

NEW QUESTION # 184
An ML engineer is using a training job to fine-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 overfitting 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. 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.
B. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
C. 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.
D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

Answer: D

NEW QUESTION # 185
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

A. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C. Use AWS Glue DataBrew built-in features to oversample the minority class.
D. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Answer: A

NEW QUESTION # 186
A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.
The company needs sub-second latency for a real-time OpenSearch dashboard.
Which architectural change will meet this requirement?

A. Replace Firehose with AWS DataSync and enhanced fan-out consumers.
B. Use zero buffering in the Firehose stream and tune the PutRecordBatch batch size.
C. Increase the Firehose buffer interval to 120 seconds.
D. Replace Firehose with Amazon SQS.

Answer: B

Explanation:
Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size.
AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low- latency ingestion.
By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.
DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.
AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.
Therefore, Option A is the correct and AWS-verified solution.

NEW QUESTION # 187
An ML company wants to monitor and analyze the API calls that its AWS resources make. The company has created an AWS CloudTrail log file that logs to an Amazon S3 bucket. The company has also created an organization in AWS Organizations to manage permissions across accounts.
The company needs to enable log file validation to ensure the integrity of its log files.
Which solution will meet these requirements?

A. Enable CloudTrail log file integrity validation.
B. Create a trail in CloudTrail for the organization.
C. Enable Amazon CloudWatch Logs delivery.
D. Create a multi-Region trail in CloudTrail.

Answer: A

Explanation:
The correct answer is A. Enable CloudTrail log file integrity validation.
AWS CloudTrail provides the ability to record API calls made to AWS services and delivers log files to an Amazon S3 bucket. For organizations that need to ensure the authenticity and integrity of these log files, AWS recommends enabling log file integrity validation. This feature applies a hash function to each log file and stores the hash separately, allowing you to verify that the logs have not been altered, deleted, or tampered with after delivery.
Enabling log file integrity validation is critical in ML operations when auditing model training pipelines, production inference calls, or system access patterns across accounts. It ensures that security-sensitive API activity is accurately recorded and verifiable. In multi-account environments managed by AWS Organizations, this validation provides an extra layer of trust when logs are consolidated from multiple accounts.
Option B, creating a multi-Region trail, ensures that API activity across regions is logged but does not inherently guarantee the integrity of logs. Option C, creating an organization trail, centralizes logging for all accounts, which is valuable for governance, but again does not automatically provide verification of log integrity. Option D, enabling CloudWatch Logs delivery, allows real-time monitoring and alerting but does not address the verification of historical log files.
By enabling log file integrity validation, organizations can cryptographically verify each log file, detect unauthorized changes, and meet compliance requirements for secure ML monitoring and auditing. This aligns with AWS best practices for ML solution monitoring, maintenance, and security, ensuring reliable tracking of model operations and API usage across distributed ML systems.

NEW QUESTION # 188
A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less.
Which SageMaker inference option will meet these requirements?

A. Batch transform
B. Serverless inference
C. Asynchronous inference
D. Real-time inference

Answer: C

NEW QUESTION # 189
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