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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 Price:	150 USD
Passing Score:	720 (scaled score 100–1000)
Real Exam Qty:	65 (50 scored, 15 unscored)
Certificate Validity Period:	3 years
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Exam Duration:	130 minutes
Exam Format:	Ordering, Multiple response, Case study, Matching, Multiple choice
Available Languages:	English, Simplified Chinese, Korean, Japanese
Recommended Training:	AWS Certified Machine Learning Engineer - Associate Official Exam Guide AWS Training and Certification
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	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 3	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 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 (Q81-Q86):

NEW QUESTION # 81
A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features. Which solution will meet these requirements?

A. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
B. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.
C. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
D. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data.Create an Amazon DynamoDB table to store the features.

Answer: B

Explanation:
Amazon SageMaker Feature Processing (via processing jobs) is used to perform feature engineering and data preparation. The engineered features can then be ingested into SageMaker Feature Store, which is a purpose-built service to manage and store ML features for reuse across training and inference. This combination directly addresses the company's requirements.

NEW QUESTION # 82
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
C. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
D. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Answer: D

Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

NEW QUESTION # 83
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.
How should the ML engineer configure the data to train the model?

A. Remove the medical conditions and test results columns.
B. Remove the age column.
C. Remove the patient ID column.
D. Remove the "Disease" target column.

Answer: C

Explanation:
Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.
AWS ML best practices recommend removing identifiers that are not causally related to the target variable.
Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.
Therefore, Option A is the correct and AWS-aligned choice.

NEW QUESTION # 84
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::NotebookInstance
B. AWS::SageMaker::Endpoint
C. AWS::SageMaker::Pipeline
D. AWS::SageMaker::Model

Answer: D

NEW QUESTION # 85
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?

A. Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
B. Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
C. Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
D. Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).

Answer: A

NEW QUESTION # 86
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