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

TopicDetails
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
  • 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
  • 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q95-Q100):

NEW QUESTION # 95
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

Answer: B


NEW QUESTION # 96
A company is developing an application that reads animal descriptions from user prompts and generates images based on the information in the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue.
Which IAM permissions should the company assign to the application's IAM role? (Select TWO.)

Answer: B,C

Explanation:
To generate images, the application must invoke a foundation model hosted on Amazon Bedrock. Invoking the Amazon Titan Image Generator requires the bedrock:InvokeModel permission. Read-only permissions such as bedrock:Get* do not allow inference execution and are insufficient.
The application also interacts with Amazon Simple Queue Service. To process messages, the application must be able to receive messages from the queue and delete them after successful processing. These actions require sqs:ReceiveMessage and sqs:DeleteMessage.
sqs:GetQueueAttributes alone does not allow message consumption. SageMaker permissions are unrelated because Bedrock is a separate service.
Therefore, permissions to invoke the model and receive/delete SQS messages are required.


NEW QUESTION # 97
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.)

Answer: B,C


NEW QUESTION # 98
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.


NEW QUESTION # 99
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?

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 # 100
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