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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
Exam Format:	Matching, Ordering, Multiple response, Case study, Multiple choice
Exam Price:	150 USD
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Passing Score:	720 (scaled score 100–1000)
Certificate Validity Period:	3 years
Exam Duration:	130 minutes
Available Languages:	English, Korean, Simplified Chinese, Japanese
Real Exam Qty:	65 (50 scored, 15 unscored)
Recommended Training:	AWS Training and Certification AWS Certified Machine Learning Engineer - Associate Official Exam Guide
Exam Registration:	AWS Certification Portal Pearson VUE Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE testing centers
Pre Condition:	Recommended: 1+ year hands-on experience with AWS services and machine learning engineering; familiarity with Amazon SageMaker and related ML services. No mandatory prerequisite exams.
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html

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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 (Q218-Q223):

NEW QUESTION # 218
A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?

A. Undersampling
B. Oversampling using SMOTE
C. Oversampling using bootstrapping
D. Class weight adjustment

Answer: B

Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE. adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario.

NEW QUESTION # 219
A company has developed a computer vision model. The company needs to deploy the model into production on Amazon SageMaker AI. The company has not hosted a model on SageMaker AI previously.
An ML engineer needs to implement a solution to track model versions. The solution also must provide recommendations about which Amazon EC2 instance types to use to host the model.
Which solution will meet these requirements?

A. Register the model in the SageMaker Model Registry. Use SageMaker Autopilot for recommendations about instance types.
B. Register the model in the SageMaker Model Registry. Use SageMaker Inference Recommender for recommendations about instance types.
C. Register the model in Amazon Elastic Container Registry (Amazon ECR). Use SageMaker Experiments for recommendations about instance types.
D. Register the model in Amazon Elastic Container Registry (Amazon ECR). Use AWS Compute Optimizer for recommendations about instance types.

Answer: B

Explanation:
Option C is correct because the requirement has two separate parts : first, the company must track model versions ; second, it needs recommendations for which instance types to use for hosting the model in SageMaker AI. AWS documentation identifies the SageMaker Model Registry as the SageMaker feature used to register and manage model packages and versions as part of the ML lifecycle. For hosting recommendations, AWS documentation says Amazon SageMaker Inference Recommender helps select the best instance type and configuration for ML models and workloads by automating benchmarking and load testing across SageMaker AI instances.
The AWS Inference Recommender documentation is especially important here because it explicitly says you can use it after you register a model to the SageMaker Model Registry with model artifacts. It also states that Inference Recommender helps choose the best endpoint type and configuration and returns recommendations that include the instance type in the resulting endpoint configuration. That is an exact match to the question's requirement to recommend hosting instance types for a model that has not yet been hosted on SageMaker AI.
The other options do not match AWS service purposes. Amazon ECR stores container images, not model versions. AWS Compute Optimizer is not the SageMaker-native service documented for inference benchmarking of ML models. SageMaker Autopilot is for automated model building, not inference instance recommendations. SageMaker Experiments tracks experiment metadata, trials, and runs, but it does not recommend deployment instance types. Therefore, the fully verified AWS-docs answer is C .

NEW QUESTION # 220
A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model. How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

A. Configure a serverless inference endpoint.
B. Configure a real-time inference endpoint.
C. Configure batch transform.
D. Configure an asynchronous inference endpoint.

Answer: B

Explanation:
To minimize response-time latency, the ML model should be deployed to a real-time inference endpoint in Amazon SageMaker. This provides low-latency predictions by keeping the model loaded and ready to handle incoming requests, which is critical for an online application serving customers in real time.

NEW QUESTION # 221
An ML engineer is using an Amazon SageMaker AI shadow test to evaluate a new model that is hosted on a SageMaker AI endpoint. The shadow test requires significant GPU resources for high performance. The production variant currently runs on a less powerful instance type.
The ML engineer needs to configure the shadow test to use a higher performance instance type for a shadow variant. The solution must not affect the instance type of the production variant.
Which solution will meet these requirements?

A. Use the CreateEndpointConfig action to define a new configuration. Specify the existing production variant in the configuration and add a separate ShadowProductionVariants list. Specify the larger instance type for the shadow variant. Use the CreateEndpoint action and pass the new configuration to the endpoint.
B. Modify the existing ProductionVariant configuration in the endpoint to include a ShadowProductionVariants list. Specify the larger instance type for the shadow variant.
C. Create a separate SageMaker AI endpoint for the shadow variant that uses the larger instance type.
Create an AWS Lambda function that routes a portion of the traffic to the shadow endpoint. Assign the Lambda function to the original endpoint.
D. Create a new endpoint configuration with two ProductionVariant definitions. Configure one definition for the existing production variant and one definition for the shadow variant with the larger instance type. Use the UpdateEndpoint action to apply the new configuration.

Answer: A

Explanation:
Amazon SageMaker AI shadow testing enables ML engineers to evaluate new model versions by sending a copy of live production traffic to a shadow variant without affecting production inference responses. AWS documentation specifies that shadow variants are configured separately from production variants and can use different instance types, including higher-performance GPU instances.
The correct approach is to create a new endpoint configuration using the CreateEndpointConfig API. This configuration includes the existing production variant and a separate ShadowProductionVariants list. The shadow variant can be assigned a larger instance type to meet GPU performance requirements while leaving the production variant unchanged. After creating the configuration, the engineer deploys it using the CreateEndpoint action.
Option A is incorrect because production variant configurations cannot be directly modified to include shadow variants. Option B is incorrect because shadow variants are not defined as standard production variants; defining two production variants would route traffic differently and could affect production behavior. Option C introduces unnecessary complexity and deviates from SageMaker's built-in shadow testing functionality.
AWS explicitly documents that shadow variants are designed to isolate testing resources, support different instance types, and ensure zero impact on production inference. Therefore, Option D is the correct and AWS- recommended solution.

NEW QUESTION # 222
A company is developing an ML model for a customer. The training data is stored in an Amazon S3 bucket in the customer ' s AWS account (Account A). The company runs Amazon SageMaker AI training jobs in a separate AWS account (Account B).
The company defines an S3 bucket policy and an IAM policy to allow reads to the S3 bucket.
Which additional steps will meet the cross-account access requirement?

A. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.
B. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.
C. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.
D. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.

Answer: A

Explanation:
For cross-account Amazon S3 access, AWS requires two components:
An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account An IAM role policy in the consuming account (Account B) that allows the service to access the bucket Amazon SageMaker training jobs assume an IAM role in the account where the job runs-in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.
The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.
Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.
AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 223
......

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