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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
  • 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 4
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q79-Q84):

NEW QUESTION # 79
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?

Answer: D

Explanation:
AWS documentation strongly recommends using custom Docker containers when ML workloads require consistent access to custom dependencies across processing jobs, training jobs, and pipelines.
By building a single Docker image that contains all required libraries and hosting it in Amazon ECR, the ML engineer ensures that every SageMaker job uses the same runtime environment. This approach eliminates the need for repetitive installation steps and avoids environment drift.
Manually installing libraries in managed containers is error-prone and not reusable across jobs. Notebook instances are not designed to host production jobs and pipelines. Running code externally breaks the SageMaker workflow and increases operational complexity.
Using a custom container is a one-time setup that provides maximum reuse with minimal ongoing effort, making it the least implementation effort option in the long run.
Therefore, Option B is the correct and AWS-recommended answer.


NEW QUESTION # 80
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

Answer: D


NEW QUESTION # 81
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 # 82
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
The correct answer is A. Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
Amazon Kinesis Data Streams is a fully managed service that can handle high-volume, real-time data streams with low latency and high scalability. By integrating Amazon Managed Service for Apache Flink (MSK for Flink) with the built-in RANDOM_CUT_FOREST (RCF) algorithm, the company can perform real-time anomaly detection directly on streaming data without building or managing custom infrastructure. RCF is designed for unsupervised anomaly detection on streaming datasets, making it ideal for financial market data that arrives continuously and at high velocity.
Option B adds operational complexity because it requires deploying a SageMaker endpoint, setting up Lambda functions, and maintaining the orchestration between Kinesis and Lambda. Option C further increases overhead by requiring self-managed Apache Kafka clusters on EC2, combined with SageMaker and Lambda orchestration. Option D introduces SQS and batch ETL processing via AWS Glue, which is not suitable for real-time anomaly detection and significantly increases latency.
Using Kinesis + Managed Flink + RCF provides a serverless, fully managed, and scalable solution with minimal operational overhead. It handles ingestion, streaming processing, and anomaly detection natively.
The architecture eliminates the need for provisioning compute clusters or managing real-time orchestration, reducing operational cost while achieving sub-second detection for thousands of JSON records per second.
This approach aligns with AWS best practices for ML solution monitoring, maintenance, and security, particularly for real-time anomaly detection in high-volume, structured or semi-structured data streams.


NEW QUESTION # 83
An ML engineer needs to deploy a trained model based on a genetic algorithm. Predictions can take several minutes, and requests can include up to 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
SageMaker Asynchronous Inference is designed for long-running inference workloads and large payloads (up to 1 GB). Requests are queued, processed asynchronously, and results are written to Amazon S3.
Real-time endpoints have payload and timeout limits. EC2 and ECS require infrastructure management, increasing operational overhead.
AWS documentation explicitly recommends asynchronous inference for workloads with large inputs and long execution times.
Therefore, Option C is the correct and most efficient solution.


NEW QUESTION # 84
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