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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
Real Exam Qty:85
Related Certifications:AWS Certified Solutions Architect - Associate
AWS Certified Data Engineer - Associate
AWS Certified Developer - Associate
Exam Format:Multiple choice, Multiple response
Certificate Validity Period:3 years
Passing Score:1000-2000 scaled score (passing score approximately 720)
Available Languages:Traditional Chinese, English, Simplified Chinese, Japanese, Korean
Exam Duration:170 minutes
Exam Price:150 USD
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:

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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q68-Q73):

NEW QUESTION # 68
An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker AI notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.
The lead ML engineer must see the latest SageMaker AI notebook updates.
Which solution will meet this requirement?

Answer: B

Explanation:
Option A is correct because the issue is that the lead ML engineer's local notebook environment does not yet contain the latest merged changes from the shared Git repository. In Git, the operation used to fetch remote updates and merge them into the local branch is git pull . AWS documentation for SageMaker notebook collaboration explains that Git repositories can be associated with SageMaker notebook instances so distributed teams can share notebooks and collaborate in a source-control environment.
AWS documentation is even more explicit in the SageMaker Git operations guide: to fetch notebooks committed by other users , you should do a pull from the project repository . That is exactly the situation described in the question. Other team members have already merged their notebook changes into the repository, and the lead engineer needs the latest notebook updates in the local SageMaker notebook environment. Therefore, running !git pull origin master is the correct command among the available choices.
The other options do not solve the problem. git commit records local changes in the user's own repository history; it does not download new remote content. git push origin master uploads local commits to the remote repository; it is used when you want to send your own changes outward, not retrieve teammates' updates. git branch only lists or manages branches and does not synchronize notebook content. Because the requirement is to see the latest merged notebook made by others , the necessary operation is a pull from the remote repository. So the best verified answer is A .


NEW QUESTION # 69
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)

Answer: C,D

Explanation:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.


NEW QUESTION # 70
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

Answer: D


NEW QUESTION # 71
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

Answer: A

Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.


NEW QUESTION # 72
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

Answer: A


NEW QUESTION # 73
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