Amazon MLA-C01 Exam Questions

Exam Name: AWS Certified Machine Learning Engineer – Associate

Exam Code: MLA-C01

Related Certification(s): Amazon Associate Certification

Certification Provider: Amazon

Actual Exam Duration: 130 Minutes

Number of MLA-C01 Practice Questions: 207 (updated: )

Expected MLA-C01 Exam Topics, as suggested by Amazon:
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: 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: 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.
Free AWS MLA-C01 Exam Actual Questions
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Q1#

An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI.
The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket.
New data files are approximately 10 GB in size.
The ML engineer also needs to track model versions for auditing.

Which solution will meet these requirements?

Q2#

An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization.
The ML engineer must follow a low-code no-code (LCNC) approach.

Which solution will meet these requirements?

Q3#

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?

Q4#

An ML engineer needs to implement a solution to host a trained ML model.
The rate of requests to the model will be inconsistent throughout the day.

The ML engineer needs a scalable solution that minimizes costs when the model is not in use.
The solution also must maintain the model’s capacity to respond to requests during times of peak usage.

Which solution will meet these requirements?

Q5#

An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization.
The ML engineer must follow a low-code no-code (LCNC) approach.

Which solution will meet these requirements?