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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 Duration:	130 minutes
Exam Price:	USD 150
Passing Score:	720/1000
Certificate Validity Period:	3 years
Available Languages:	Japanese, English, Simplified Chinese, Korean
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Format:	Matching, Ordering, Multiple response, Multiple choice
Related Certifications:	AWS Certified AI Practitioner AWS Certified Solutions Architect – Associate AWS Certified DevOps Engineer – Professional AWS Certified Data Engineer – Associate
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:

Topic	Details
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	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	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 (Q30-Q35):

NEW QUESTION # 30
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

A. Area Under the ROC Curve (AUC)
B. Mean absolute error (MAE)
C. F1 score
D. Accuracy

Answer: B

Explanation:
Predicting apartment prices is a regression problem, where the target variable is continuous. AWS documentation states that classification metrics such as accuracy, AUC, and F1 score are not appropriate for regression tasks.
Mean Absolute Error (MAE) measures the average absolute difference between predicted values and actual values. MAE is easy to interpret because it is expressed in the same units as the target variable (for example, dollars), making it especially useful for business-facing problems like price prediction.
AWS best practices recommend MAE for evaluating regression models when understanding average prediction error magnitude is important and when robustness to outliers is desired.
Therefore, Option D is the correct and AWS-aligned answer.

NEW QUESTION # 31
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?

A. Create an S3 Lifecycle rule to start the pipeline every time a new object is uploaded to the S3 bucket.
B. Create an Amazon CloudWatch rule that runs on a schedule to start the pipeline every 30 days.
C. Create an AWS Lambda function that runs one time each week to poll the S3 bucket for new files.
Invoke the Lambda function asynchronously. Configure the Lambda function to start the pipeline if the function detects new data.
D. Create an Amazon EventBridge rule to start an AWS Step Functions TrainingStep every time a new object is uploaded to the S3 bucket.

Answer: D

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 # 32
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

A. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
B. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
C. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
D. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.

Answer: B

NEW QUESTION # 33
Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?

A. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
B. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
C. Use AWS Glue to transform the categorical data into numerical data.
D. Use AWS Glue to transform the numerical data into categorical data.

Answer: B

NEW QUESTION # 34
An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

A. Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements.
B. Train the deep learning model directly on the raw data. Let the model infer the labels by itself.
C. Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.
D. Use Amazon Rekognition to automatically label the dataset.
E. Use Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.

Answer: A,C

Explanation:
To achieve the highest labeling accuracy with controlled employee-only access, the ML engineer should use Amazon SageMaker Ground Truth to define the annotation job and then assign it to a private workforce of employees for labeling and review. This ensures high-quality, secure labeling restricted to authorized personnel.

NEW QUESTION # 35
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