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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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.
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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q178-Q183):

NEW QUESTION # 178
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

Answer: C

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.


NEW QUESTION # 179
A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
For real-time video content moderation with minimal operational overhead, AWS documentation recommends using fully managed, purpose-built AI services. Amazon Rekognition provides real-time video analysis capabilities, including content moderation, unsafe content detection, and label recognition for live video streams.
By integrating Rekognition with AWS Lambda, the company can automatically process video frames, extract moderation metadata, and take immediate action (such as flagging or stopping a stream) without managing servers, models, or infrastructure. This serverless architecture scales automatically and minimizes operational complexity.
Option B introduces unnecessary complexity. While Amazon Bedrock LLMs are powerful, they are not required for image-based moderation tasks that Rekognition already handles natively.
Option C is incorrect because using Amazon SageMaker would require model training, endpoint management, and scaling, significantly increasing operational overhead.
Option D is incorrect because Amazon Transcribe and Amazon Comprehend are designed for audio and text analysis, not image or video frame moderation.
Therefore, Amazon Rekognition with AWS Lambda is the most efficient, scalable, and low-maintenance solution for real-time video moderation during live streaming events.


NEW QUESTION # 180
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

Answer: B

Explanation:
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.


NEW QUESTION # 181
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

Answer: A

Explanation:
The correct answer is C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
Amazon SageMaker Model Monitor is the AWS-recommended solution for automatically detecting data drift in ML pipelines. Data drift occurs when the statistical properties of input features change over time, potentially reducing model accuracy. Model Monitor continuously analyzes incoming data and compares it to the baseline training dataset to identify deviations. It can track both feature distributions and prediction quality metrics.
Once Model Monitor detects data drift, it can trigger automated workflows using AWS Lambda or Amazon EventBridge. An AWS Lambda function can initiate a SageMaker training job to re-train the model using updated datasets, ensuring the ML model remains accurate and reliable. This setup fully automates the response to drift events, meeting the requirement of automatically initiating re-training.
Option A (AWS Glue) is designed for ETL processes but does not natively detect ML-specific data drift.
Option B (Amazon Managed Service for Apache Flink) can process streaming data but does not provide native drift detection for ML pipelines. Option D (Amazon QuickSight anomaly detection) focuses on business intelligence and visual anomaly detection, not automated re-training workflows for ML models.
By integrating SageMaker Model Monitor with Lambda, the ML engineer can maintain model performance proactively, implement automated re-training, and align with AWS best practices for ML solution monitoring, maintenance, and security. This approach ensures continuous validation of model inputs and outputs, reduces operational overhead, and prevents degradation in production ML performance due to unseen data changes.


NEW QUESTION # 182
A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model's performance has degraded over time.
Which solution can the company use to identify model drift in the future?

Answer: A

Explanation:
AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.
AWS documentation clearly specifies the required order:
* Create a baseline using training data statistics
* Create a monitoring schedule to compare incoming data against the baseline Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.
Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.
Therefore, Option B is the correct and AWS-verified answer.


NEW QUESTION # 183
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