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

Topic	Details
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	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 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 (Q22-Q27):

NEW QUESTION # 22
A company needs to deploy a custom-trained classification ML model on AWS. The model must make near real-time predictions with low latency and must handle variable request volumes.
Which solution will meet these requirements?

A. Use Amazon API Gateway to receive prediction requests. Use an Amazon S3 bucket to host and serve the model.
B. Create an Amazon SageMaker AI batch transform job to process inference requests in batches.
C. Launch AWS Deep Learning AMIs (DLAMI) on two Amazon EC2 instances. Run the instances behind an Application Load Balancer.
D. Deploy an Amazon SageMaker AI endpoint. Configure auto scaling for the endpoint.

Answer: D

Explanation:
For near real-time inference with low latency and variable traffic, AWS recommends deploying models to managed SageMaker endpoints. By enabling auto scaling, the endpoint automatically adjusts the number of instances based on request volume, ensuring consistent performance while optimizing cost.
Amazon SageMaker Endpoints abstracts infrastructure management, health checks, scaling, and model deployment. This provides lower operational overhead than managing EC2 instances manually.
Batch transform is for offline inference. API Gateway with S3 cannot serve ML models. EC2-based deployments require manual scaling and monitoring.
Therefore, a SageMaker endpoint with auto scaling is the correct solution.

NEW QUESTION # 23
An IoT company uses Amazon SageMaker to train and test an XGBoost model for object detection. ML engineers need to monitor performance metrics when they train the model with variants in hyperparameters. The ML engineers also need to send Short Message Service (SMS) text messages after training is complete.
Which solution will meet these requirements?

A. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.
B. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
C. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.
D. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.

Answer: A

NEW QUESTION # 24
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop a chat interface for internal technical documentation.
The documentation consists of dozens of text files totaling several megabytes and is updated frequently.
Which solution will meet these requirements MOST cost-effectively?

A. Fine-tune an LLM in Amazon Bedrock with the documentation.
B. Use Amazon Bedrock guardrails to integrate documentation.
C. Train a new LLM in Amazon Bedrock using the documentation.
D. Upload the documentation to an Amazon Bedrock knowledge base and use it as context during inference.

Answer: D

Explanation:
AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.
A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.
Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data.
Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.
AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.
Therefore, Option D is the correct and most cost-effective solution.

NEW QUESTION # 25
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

A. Implement padding to ensure that all color feature vectors have the same length.
B. Perform dimensionality reduction on the color categories.
C. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
D. Apply label encoding to the color categories. Automatically assign each color a unique integer.

Answer: C

Explanation:
One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.

NEW QUESTION # 26
A company uses 10 Reserved Instances of accelerated instance types to serve the current version of an ML model. An ML engineer needs to deploy a new version of the model to an Amazon SageMaker real-time inference endpoint.
The solution must use the original 10 instances to serve both versions of the model. The solution also must include one additional Reserved Instance that is available to use in the deployment process. The transition between versions must occur with no downtime or service interruptions.
Which solution will meet these requirements?

A. Configure a blue/green deployment with all-at-once traffic shifting.
B. Configure a shadow test with a traffic sampling percentage of 10%.
C. Configure a rolling deployment with a rolling batch size of 1.
D. Configure a blue/green deployment with canary traffic shifting and a size of 10%.

Answer: C

NEW QUESTION # 27
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