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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 Format:Ordering, Multiple choice, Multiple response, Matching
Certificate Validity Period:3 years
Related Certifications:AWS Certified AI Practitioner
AWS Certified Solutions Architect – Associate
AWS Certified DevOps Engineer – Professional
AWS Certified Data Engineer – Associate
Available Languages:Korean, Simplified Chinese, English, Japanese
Passing Score:720/1000
Real Exam Qty:65 scored questions + 15 unscored questions
Exam Duration:130 minutes
Exam Price:USD 150
Recommended Training:Amazon SageMaker Documentation
AWS Skill Builder - ML Engineer Associate Exam Prep
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
  • 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 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
  • 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 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 (Q168-Q173):

NEW QUESTION # 168
A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker algorithm should the company choose for the recommendation model?

Answer: B

Explanation:
The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation model in this scenario.


NEW QUESTION # 169
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.
Which solution will meet this requirement with the LEAST development effort?

Answer: D


NEW QUESTION # 170
A customer call center uses Amazon Transcribe to convert hundreds of audio recordings of conversations between customers and support agents to text files. The call center wants to use the text files to train an ML model. To comply with industry regulations, the call center must remove customer names, addresses, and phone numbers from the training text files.
Which solution will meet these requirements with the LEAST development effort?

Answer: B

Explanation:
Option B is correct because AWS Glue provides a built-in Detect PII transform that can detect, mask, or remove personally identifiable information with minimal custom development. AWS documentation says the Detect PII transform can process predefined AWS-managed PII entity types and supports actions such as removing or masking values. The examples in AWS docs explicitly mention sensitive entities such as phone numbers and addresses, which directly match the problem statement.
The question specifically asks for the least development effort. That wording makes AWS Glue Detect PII the strongest answer because it is a native transformation capability rather than a custom code-heavy workflow.
AWS also documents fine-grained sensitive data detection features that let you apply actions per entity type, improving usability and reducing the need to build custom parsing and redaction logic yourself. This is much easier than creating Lambda-based transformation code or custom text-cleaning logic inside another ML preprocessing tool.
The other options are less suitable. Amazon Bedrock Guardrails is not the standard AWS service documented for bulk ETL-style redaction of training text files in this context. S3 Object Lambda would require more custom engineering to inspect and redact each object. SageMaker Data Wrangler custom transformation would also involve extra implementation work compared with using a purpose-built Glue transform. Because the call center already has text output and simply needs regulated fields like names, addresses, and phone numbers removed before training, the AWS-native low-effort solution is AWS Glue Detect PII. Therefore, the best verified answer is B.


NEW QUESTION # 171
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

Answer: A


NEW QUESTION # 172
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

Answer: B

Explanation:
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.


NEW QUESTION # 173
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