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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer - Associate
Exam Number:	MLA-C01
Exam Format:	Multiple response, Multiple choice
Exam Price:	150 USD
Exam Duration:	170 minutes
Related Certifications:	AWS Certified Solutions Architect - Associate AWS Certified Data Engineer - Associate AWS Certified Developer - Associate
Certificate Validity Period:	3 years
Passing Score:	1000-2000 scaled score (passing score approximately 720)
Real Exam Qty:	85
Available Languages:	English, Traditional Chinese, Simplified Chinese, Korean, Japanese
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored (Pearson VUE) or in-person testing center
Pre Condition:	Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q164-Q169):

NEW QUESTION # 164
A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an AWS Lambda function that scans the S3 bucket and initiates the pipeline when new data is uploaded.
B. Create an S3 lifecycle rule to transfer the data to the SageMaker AI training instance and initiate training.
C. Use Amazon Managed Workflows for Apache Airflow (MWAA) to orchestrate the pipeline when new data is uploaded.
D. Create an Amazon EventBridge rule that matches S3 upload events and configures the SageMaker pipeline as the target.

Answer: D

Explanation:
AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.
Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.
Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event- based trigger.
AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 165
An ML engineer wants to deploy a workflow that processes streaming IoT sensor data and periodically retrains ML models. The most recent model versions must be deployed to production.
Which service will meet these requirements?

A. AWS Lambda
B. Amazon Managed Workflows for Apache Airflow (MWAA)
C. Amazon SageMaker Pipelines
D. Apache Spark

Answer: C

Explanation:
Amazon SageMaker Pipelines is purpose-built for orchestrating end-to-end ML workflows, including data ingestion, training, evaluation, and deployment. It supports automation, versioning, and deployment of the latest model versions.
MWAA orchestrates general workflows but lacks ML-native features. Lambda cannot handle long-running ML training. Spark processes data but does not manage ML lifecycle.
Therefore, Option A is the correct AWS-native solution.

NEW QUESTION # 166
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

A. Adjust the model's parameters and hyperparameters.
B. Include additional data in the existing training set for the model. Retrain and redeploy the model.
C. Initiate a manual Model Monitor job that uses the most recent production data.
D. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Answer: D

Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.

NEW QUESTION # 167
A company wants to evaluate a new ML model architecture to understand its performance before deploying the model to production. The company wants to use Amazon SageMaker AI shadow testing.
The company needs to analyze the performance metrics of the shadow model and the production model without affecting the existing production endpoint. The analysis must use real-time inference requests.
Select and order the correct steps to implement shadow testing and compare the model variants in SageMaker AI. Select each step one time or not at all (Select and order Three)

Answer:

Explanation:

Explanation:
Step 1: Update the existing endpoint with a shadow variant. Pick a suitable duration. Start the shadow test.
Step 2: Create a shadow test.
Step 3: Use the SageMaker shadow testing dashboard to analyze the performance differences between the variants.
This is the correct order because SageMaker shadow testing can run against an existing production endpoint.
The production variant continues to receive and respond to inference requests, while the shadow variant receives replicated real-time requests but does not return responses to users. That satisfies the requirement to analyze the new model with real inference traffic without affecting the production endpoint. AWS also states that the SageMaker shadow testing dashboard provides side-by-side comparison metrics for the production and shadow variants, such as latency and error rate.
Do not choose "Create a new endpoint that includes the production model and the shadow model," because the question explicitly says the analysis must not affect the existing production endpoint. Also, do not use the Amazon SageMaker Model Monitoring dashboard here; the specific feature for comparing variants in this scenario is the SageMaker shadow testing dashboard.

NEW QUESTION # 168
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket.
The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model.
The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference.
Which solution will meet these requirements?

A. Use the Generate Data Insights function in SageMaker Data Wrangler.
B. Use Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
C. Use AWS Lake Formation. Apply tag-based control on the data.
D. Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.

Answer: D

NEW QUESTION # 169
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