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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
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: A

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 # 12
A hospital wants to predict patient outcomes for the coming year An ML engineer must improve several existing ML models that currently perform poorly.
Select the correct regularization method from the following list to improve each model Select each regularization method one time, more than one time, or not at all. (Select THREE.)
* L1 regularization
* L2 regularization
* Early stopping

Answer:

Explanation:

Explanation:
Linear regression model whose coefficients should shrink but not become zero answer: L2 regularization AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.
Polynomial regression model with irrelevant polynomial terms that should be eliminated answer: L1 regularization AWS says L1 reduces the number of features used by pushing small weights to zero , which matches elimination of irrelevant terms.
Logistic regression model that has highly correlated features to eliminate highly redundant predictors L1 regularization This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features , but because the question explicitly says eliminate highly redundant predictors , L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.


NEW QUESTION # 13
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

Answer: B


NEW QUESTION # 14
A company has an existing Amazon SageMaker AI model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to minimize the risk of v2 generating incorrect output in production and must prevent any disruption of production traffic during the change.
Which solution will meet these requirements?

Answer: D

Explanation:
AWS recommends SageMaker shadow testing as the safest way to validate a new model version using real production traffic without impacting production responses. A shadow variant receives a copy of inference requests but does not return predictions to end users. This completely eliminates the risk of exposing incorrect predictions.
Options A and B are canary deployments. While useful, they still allow v2 to return responses to real users, which violates the requirement to prevent disruption. Option C sends 100% of traffic to v2 externally and is risky.
Shadow variants are explicitly designed to minimize risk while using real data, making them the AWS best practice for pre-production validation.
Therefore, Option D is correct.


NEW QUESTION # 15
A company deployed an ML model that uses the XGBoost algorithm to predict product failures.
The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data.
An AWS Lambda function provides the predictions to the company's application.
An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time.
Which solution will meet these requirements?

Answer: C


NEW QUESTION # 16
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