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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
Real Exam Qty:85
Available Languages:Korean, Traditional Chinese, English, Japanese, Simplified Chinese
Related Certifications:AWS Certified Data Engineer - Associate
AWS Certified Solutions Architect - Associate
AWS Certified Developer - Associate
Exam Duration:170 minutes
Certificate Validity Period:3 years
Exam Price:150 USD
Passing Score:1000-2000 scaled score (passing score approximately 720)
Exam Format:Multiple response, Multiple choice
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
  • 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
  • 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 (Q123-Q128):

NEW QUESTION # 123
An ML engineer is analyzing a classification dataset before training a model in Amazon SageMaker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre- training bias metric.
Which metric will meet this requirement?

Answer: A

Explanation:
In Amazon SageMaker AI, identifying bias in machine learning datasets before model training is a critical step to ensure fairness and reliability of predictions. This process is referred to as pre-training bias analysis, and it focuses on understanding whether the training data itself introduces bias-particularly through imbalanced class labels or sensitive attributes.
The Difference in Proportions of Labels (DPL) is a pre-training bias metric specifically designed to measure class imbalance. DPL compares the proportion of a specific label (such as a positive outcome) across different groups or classes within a dataset. If one class or group is overrepresented relative to another, the DPL value will deviate significantly from zero, clearly indicating imbalance. AWS documentation highlights DPL as a key metric used by SageMaker Clarify to detect label imbalance prior to model training.
By contrast, Mean Squared Error (MSE) is a regression evaluation metric used after model training to measure prediction error, not dataset bias. Silhouette score is an unsupervised learning metric used to evaluate clustering quality, making it irrelevant for supervised classification bias detection. Structural Similarity Index Measure (SSIM) is an image-quality metric used in computer vision tasks and has no application in dataset bias analysis.
Using DPL allows ML engineers to proactively detect and address skewed label distributions-such as by re- sampling, re-weighting, or collecting additional data-before training begins. This aligns with AWS best practices for responsible AI and helps reduce the risk of biased predictions that could negatively impact real- world decision-making.
Therefore, Difference in Proportions of Labels (DPL) is the correct and AWS-recommended metric for confirming class imbalance during pre-training bias analysis in Amazon SageMaker AI.


NEW QUESTION # 124
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a retraining job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

Answer: C

Explanation:
AWS recommends Amazon SageMaker Model Monitor as the native service for detecting data drift, model drift, and bias drift in deployed ML models. Model Monitor continuously compares incoming inference data against a baseline dataset captured during training.
When Model Monitor detects drift beyond configured thresholds, it can emit Amazon CloudWatch events.
These events can trigger an AWS Lambda function, which is a common AWS-documented pattern for orchestrating automated workflows such as model retraining.
This Lambda function can then initiate a SageMaker Pipeline execution, starting a retraining job with updated data. This architecture aligns with AWS best practices for building automated, event-driven ML pipelines.
Option A is incorrect because AWS Glue is designed for data cataloging and ETL, not for ML-specific drift detection. Option B is unnecessary and overly complex for this use case. Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence analytics, not ML model monitoring.
AWS documentation explicitly positions SageMaker Model Monitor + Lambda automation as the recommended approach for continuous ML monitoring and retraining.
Therefore, Option C is the correct and AWS-verified answer.


NEW QUESTION # 125
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
Amazon Comprehend provides pre-built functionality for key phrase extraction and can identify meaningful keywords from documents with minimal setup or operational overhead. It eliminates the need for manual preprocessing, stemming, or stop-word removal and does not require custom model development or infrastructure management. This makes it the most efficient and low-maintenance solution for the task.


NEW QUESTION # 126
An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII.
Which solution will meet these requirements in the MOST operationally efficient way?

Answer: C


NEW QUESTION # 127
A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
For real-time video content moderation with minimal operational overhead, AWS documentation recommends using fully managed, purpose-built AI services. Amazon Rekognition provides real-time video analysis capabilities, including content moderation, unsafe content detection, and label recognition for live video streams.
By integrating Rekognition with AWS Lambda, the company can automatically process video frames, extract moderation metadata, and take immediate action (such as flagging or stopping a stream) without managing servers, models, or infrastructure. This serverless architecture scales automatically and minimizes operational complexity.
Option B introduces unnecessary complexity. While Amazon Bedrock LLMs are powerful, they are not required for image-based moderation tasks that Rekognition already handles natively.
Option C is incorrect because using Amazon SageMaker would require model training, endpoint management, and scaling, significantly increasing operational overhead.
Option D is incorrect because Amazon Transcribe and Amazon Comprehend are designed for audio and text analysis, not image or video frame moderation.
Therefore, Amazon Rekognition with AWS Lambda is the most efficient, scalable, and low-maintenance solution for real-time video moderation during live streaming events.


NEW QUESTION # 128
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