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

Topic	Details
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 (Q89-Q94):

NEW QUESTION # 89
A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an Amazon EventBridge rule that matches S3 upload events and configures the SageMaker pipeline as the target.
B. Use Amazon Managed Workflows for Apache Airflow (MWAA) to orchestrate the pipeline when new data is uploaded.
C. Create an S3 lifecycle rule to transfer the data to the SageMaker AI training instance and initiate training.
D. Create an AWS Lambda function that scans the S3 bucket and initiates the pipeline when new data is uploaded.

Answer: A

Explanation:
AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.
Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.
Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event- based trigger.
AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 90
Hotspot Question
An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
- Random Cut Forest (RCF) algorithm
- Semantic segmentation algorithm
- Sequence-to-Sequence (seq2seq) algorithm

Answer:

Explanation:

NEW QUESTION # 91
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. 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.
B. 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.
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 Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.

Answer: D

NEW QUESTION # 92
A company is training a deep learning model to detect abnormalities in images. The company has limited GPU resources and a large hyperparameter space to explore. The company needs to test different configurations and avoid wasting computation time on poorly performing models that show weak validation accuracy in early epochs.
Which hyperparameter optimization strategy should the company use?

A. Exhaustive search without early stopping
B. Grid search across all possible combinations
C. Manual tuning of each parameter individually
D. Bayesian optimization with early stopping

Answer: D

Explanation:
When GPU resources are limited and the hyperparameter search space is large, AWS documentation strongly recommends Bayesian optimization combined with early stopping. Bayesian optimization uses past evaluation results to intelligently select the next set of hyperparameters to test, focusing exploration on promising regions of the search space rather than testing all combinations.
In Amazon SageMaker, Bayesian optimization is the default and recommended strategy for hyperparameter tuning jobs. It significantly reduces the number of training runs required compared to grid or random search, making it highly cost-efficient for deep learning workloads.
Early stopping further improves efficiency by terminating training jobs that show poor validation performance in early epochs. This prevents wasted GPU time on configurations that are unlikely to perform well. AWS explicitly documents early stopping as a key feature for controlling training cost and duration.
Grid search and exhaustive search are computationally expensive and impractical for large hyperparameter spaces. Manual tuning is slow, error-prone, and does not scale.
By combining Bayesian optimization with early stopping, the company can rapidly converge on high- performing hyperparameter configurations while minimizing resource usage.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 93
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company
' s training jobs? (Choose two.)

A. Use Amazon SageMaker Ground Truth for data labeling.
B. Deploy models by using AWS Lambda functions.
C. Use AWS Trainium instances for training.
D. Use PyTorch or TensorFlow with the distributed training option.
E. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.

Answer: C,E

Explanation:
SageMaker Debugger can identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainium instances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 94
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