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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 (Q15-Q20):

NEW QUESTION # 15
A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic.
Which solution will meet these requirements?

Answer: D

Explanation:
Option A is correct because Amazon SageMaker AI supports automatic scaling for hosted models , and AWS documentation states that auto scaling dynamically adjusts the number of instances provisioned for a model in response to workload changes. When traffic increases, auto scaling brings more instances online; when traffic decreases, it removes unneeded capacity. This directly matches the requirement to keep the recommendation endpoint available during an expected increase in user traffic.
AWS also documents that SageMaker integrates with Application Auto Scaling , and you can scale SageMaker endpoint variants by using target tracking , step scaling , and scheduled scaling policies. That means the service is purpose-built for this scenario: maintaining availability and capacity as inference demand changes over time. Since the model is already being served from a SageMaker endpoint, the most direct and AWS-recommended solution is to configure endpoint auto scaling rather than redesigning the deployment.
The other options do not address the requirement as effectively. Creating a new endpoint does not automatically handle fluctuating traffic and adds unnecessary operational complexity. SageMaker Neo optimizes models for inference performance on supported hardware, but it does not provide elastic capacity management. Attaching an Auto Scaling group to a SageMaker endpoint is incorrect because SageMaker endpoints do not use EC2 Auto Scaling groups directly; scaling is handled through SageMaker's integration with Application Auto Scaling. Therefore, to maintain availability during traffic spikes, the best AWS- documented answer is A: configure auto scaling on the SageMaker AI endpoint .


NEW QUESTION # 16
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 17
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Answer: D

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.


NEW QUESTION # 18
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

Answer: A

Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.


NEW QUESTION # 19
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.)

Answer: A,B

Explanation:
For near real-time predictions, AWS documentation recommends Amazon SageMaker real-time inference endpoints. These endpoints support automatic scaling based on metrics such as ConcurrentInvocationsPerInstance, ensuring low latency and high availability during traffic spikes.
For long-running historical analysis, SageMaker Processing jobs are the appropriate solution. Processing jobs are designed for batch workloads, can run for hours, and integrate cleanly with SageMaker pipelines.
Scheduling them with Amazon EventBridge provides a fully managed, scalable, and serverless solution.
AWS Lambda is unsuitable for multi-hour workloads. EC2 Auto Scaling adds unnecessary infrastructure management overhead.
Therefore, Options A and C together meet all requirements and align with AWS best practices.


NEW QUESTION # 20
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