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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
Certificate Validity Period:	3 years
Exam Duration:	130 minutes
Available Languages:	English, Simplified Chinese, Korean, Japanese
Exam Format:	Matching, Multiple choice, Multiple response, Ordering
Real Exam Qty:	65 scored questions + 15 unscored questions
Related Certifications:	AWS Certified DevOps Engineer – Professional AWS Certified Data Engineer – Associate AWS Certified AI Practitioner AWS Certified Solutions Architect – Associate
Exam Price:	USD 150
Passing Score:	720/1000
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	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 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	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 (Q150-Q155):

NEW QUESTION # 150
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow.
The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
B. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
D. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.

Answer: D

NEW QUESTION # 151
A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance.
Which solution will meet these requirements?

A. Create an Amazon CloudWatch dashboard that includes the metrics for processing steps in the nightly batch inference job. Compare the baseline resource metrics. Share the dashboard link.
B. Create a SageMaker AI pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data.
C. Use AWS Glue DataBrew to create a custom recipe job that uses the Numerical Statistics data quality check for the model file. Generate the results.
D. Schedule a monitoring job in Amazon SageMaker Model Monitor. Generate the monitoring results for the model and data.

Answer: D

Explanation:
Option A is correct because Amazon SageMaker Model Monitor is the AWS service specifically built to monitor data quality and model quality for ML models in production. AWS documentation states that Model Monitor supports continuous monitoring with a batch transform job that runs regularly and also supports on-schedule monitoring for asynchronous batch transform jobs . That aligns directly with the scenario of a hospital running a nightly batch inference job and needing a daily report on both the incoming data and the model's predictive performance.
AWS documentation also separates the two monitoring needs very clearly. Data quality monitoring can be scheduled for batch transform jobs by using DefaultModelMonitor with a BatchTransformInput. Model quality monitoring can also be scheduled for batch transform jobs by using ModelQualityMonitor, which compares predictions against actual ground-truth labels stored in Amazon S3. Since the question explicitly asks for both model data quality and model performance , SageMaker Model Monitor is the documented feature that covers both requirements together.
Option B is not sufficient because CloudWatch dashboards show operational and resource metrics, not the full ML-specific data quality and model quality reports required here. Option C is incorrect because AWS Glue DataBrew is for data preparation and profiling, not model performance monitoring. Option D is partially plausible because SageMaker Pipelines integrates with QualityCheck steps and can run monitoring jobs on demand, but the AWS docs position Model Monitor as the native solution for scheduled monitoring of production batch inference workloads. Therefore, the best AWS-documented answer is A .

NEW QUESTION # 152
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

A. Apply statistics from a well-known dataset to normalize the production samples.
B. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
C. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

Answer: C

NEW QUESTION # 153
A company wants to evaluate a new ML model architecture to understand its performance before deploying the model to production. The company wants to use Amazon SageMaker AI shadow testing.
The company needs to analyze the performance metrics of the shadow model and the production model without affecting the existing production endpoint. The analysis must use real-time inference requests.
Select and order the correct steps to implement shadow testing and compare the model variants in SageMaker AI. Select each step one time or not at all (Select and order Three)

Answer:

Explanation:

Explanation:
Step 1: Update the existing endpoint with a shadow variant. Pick a suitable duration. Start the shadow test.
Step 2: Create a shadow test.
Step 3: Use the SageMaker shadow testing dashboard to analyze the performance differences between the variants.
This is the correct order because SageMaker shadow testing can run against an existing production endpoint.
The production variant continues to receive and respond to inference requests, while the shadow variant receives replicated real-time requests but does not return responses to users. That satisfies the requirement to analyze the new model with real inference traffic without affecting the production endpoint. AWS also states that the SageMaker shadow testing dashboard provides side-by-side comparison metrics for the production and shadow variants, such as latency and error rate.
Do not choose "Create a new endpoint that includes the production model and the shadow model," because the question explicitly says the analysis must not affect the existing production endpoint. Also, do not use the Amazon SageMaker Model Monitoring dashboard here; the specific feature for comparing variants in this scenario is the SageMaker shadow testing dashboard.

NEW QUESTION # 154
A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI.
Users upload high-resolution images to the tool.
The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete.
Which solution will meet these requirements?

A. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
B. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.
C. Store the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
D. Store the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.

Answer: B

Explanation:
This use case involves large payloads (high-resolution images), variable processing time, and a requirement to notify users upon completion. AWS documentation recommends Amazon SageMaker asynchronous inference for exactly this scenario.
Asynchronous inference allows clients to submit inference requests without waiting for a response. The request payload is stored in Amazon S3, and the inference result is written back to S3 when processing is complete. This approach is designed for workloads that cannot meet real-time latency requirements and where payload sizes exceed real-time limits.
SageMaker asynchronous inference integrates natively with Amazon SNS to send notifications when inference completes or fails, which directly satisfies the user notification requirement.
Batch transform jobs are intended for offline, scheduled processing of large datasets and do not provide built- in user notification mechanisms. Amazon EFS is not required because SageMaker natively integrates with S3 for inference input and output.
AWS explicitly documents asynchronous inference as the preferred solution for large image inference with completion notifications.
Therefore, Option B is the correct and AWS-verified answer.

NEW QUESTION # 155
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