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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:MLA-C01
Exam Price:USD 150
Available Languages:Japanese, Simplified Chinese, Korean, English
Exam Format:Multiple choice, Matching, Ordering, Multiple response
Exam Duration:130 minutes
Real Exam Qty:65 scored questions + 15 unscored questions
Passing Score:720/1000
Certificate Validity Period:3 years
Related Certifications:AWS Certified Data Engineer – Associate
AWS Certified DevOps Engineer – Professional
AWS Certified Solutions Architect – Associate
AWS Certified AI Practitioner
Recommended Training:AWS Skill Builder - ML Engineer Associate Exam Prep
Amazon SageMaker Documentation
Exam Registration:AWS Certification Registration
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored or test center exam
Pre Condition:Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q208-Q213):

NEW QUESTION # 208
An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket.
The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours.
Which solution will meet these requirements?

Answer: C

Explanation:
The requirement is event-driven automation when new data arrives in Amazon S3, followed by training and model registration. Amazon EventBridge natively supports S3 object creation events and can trigger downstream workflows immediately.
By using EventBridge to start an AWS Step Functions workflow that includes a training step and a SageMaker Model Registry registration step, the pipeline runs automatically as soon as new data is uploaded-well within the 24-hour requirement.
Option A introduces unnecessary polling and delay. Option B is time-based and does not ensure alignment with data readiness. Option C is invalid because S3 Lifecycle rules manage object transitions, not workflow execution.
Therefore, EventBridge-triggered Step Functions is the correct solution.


NEW QUESTION # 209
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: C

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 # 210
A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.
Which solution will optimize the scaling process without affecting response times?

Answer: C

Explanation:
Amazon SageMaker auto scaling uses cooldown periods to control how frequently scaling activities occur.
When scale-out happens too quickly, new instances may receive traffic before they are fully initialized, leading to inefficiencies and latency.
AWS documentation recommends increasing the scale-out cooldown period to give newly launched instances sufficient time to initialize and become healthy before additional scaling events occur. This ensures stable performance during traffic spikes without impacting response times.
Multi-model endpoints address model hosting efficiency, not scaling timing. API Gateway and Lambda add unnecessary latency and complexity. Decreasing scale-in cooldown does not address scale-out issues.
Therefore, Option D is the correct and AWS-aligned solution.


NEW QUESTION # 211
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model.
The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

Answer: B


NEW QUESTION # 212
A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

Answer: B


NEW QUESTION # 213
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