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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 Price:	USD 150
Passing Score:	720/1000
Exam Duration:	130 minutes
Available Languages:	Simplified Chinese, English, Korean, Japanese
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Format:	Matching, Ordering, Multiple choice, Multiple response
Related Certifications:	AWS Certified Data Engineer – Associate AWS Certified AI Practitioner AWS Certified DevOps Engineer – Professional AWS Certified Solutions Architect – Associate
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	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 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	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 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 (Q51-Q56):

NEW QUESTION # 51
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload.
Configure the pipeline as the target of the rule.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
C. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
D. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.

Answer: A

NEW QUESTION # 52
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?

A. Neural Topic Model (NTM)
B. Linear learner
C. #-means clustering
D. LightGBM

Answer: B

Explanation:
Why Linear Learner?
* SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
* Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
* Automatically weights minority and majority classes.
* Supports both classification and regression tasks.
* Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
* Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
* Configure the hyperparameters to enable balanced class weights.
* Train the model with the balanced dataset created using SageMaker Data Wrangler.

NEW QUESTION # 53
A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents.
Which solution will meet this requirement with the LEAST operational effort?

A. Use Amazon Comprehend to analyze text-based driver feedback and identify distractions.
B. Integrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions.
C. Use Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions.
D. Use Amazon SageMaker AI to customize an AI model to monitor driver behavior and identify distractions.

Answer: C

Explanation:
Option A is correct because Amazon Rekognition provides a built-in EyeDirection attribute that indicates the direction a person's eyes are gazing, expressed through pitch and yaw. AWS documentation describes EyeDirection as showing where the eyes are looking independently of head pose. For a driver-monitoring use case, this is directly useful for identifying whether a driver is looking away from the road and may be distracted.
AWS also announced that Rekognition's eye gaze direction detection is intended to support safety use cases and to help identify where users focus their attention. That makes it a strong match for monitoring in-vehicle cameras for distraction detection. Because Rekognition is a fully managed service with ready-to-use computer vision capabilities, it requires far less operational effort than collecting data, labeling it, training a custom model in SageMaker, and maintaining that model over time.
The other options are less appropriate. Option B could work technically, but building and maintaining a custom SageMaker model would require significantly more engineering and operational work than using a managed Rekognition feature. Option C introduces unnecessary complexity by adding a third-party system when AWS already provides a directly relevant managed capability. Option D is unrelated because Amazon Comprehend analyzes text, while the input here is camera footage. Since the question asks for the solution with the least operational effort , the best AWS-aligned answer is to use the managed Rekognition eye gaze detection capability. Therefore, the fully verified answer is A .

NEW QUESTION # 54
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.
Which solution will meet these requirements with the LEAST manual effort?

A. Use AWS Glue to run the ETL job. Use the job to discover the schema and to store the associated metadata in the AWS Glue Data Catalog.
B. Create an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the schema and to store the associated metadata in an S3 bucket.
C. Launch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the job to discover the schema and to store the associated metadata in Amazon Redshift.
D. Create an ETL pipeline by using Amazon Athena integrated with AWS Step Functions. Use the pipeline to run the ETL job to discover the schema and to store the associated metadata in an S3 bucket.

Answer: A

Explanation:
Option A is correct because AWS Glue is the AWS-native managed ETL service built specifically to discover schema , run ETL jobs , and store metadata in the AWS Glue Data Catalog . AWS documentation states that Glue crawlers can automatically discover and catalog new or updated data sources , and that the Data Catalog automatically captures and manages schema metadata. This directly matches the requirement to run an ETL job on data in Amazon S3, discover the schema, and store the metadata with the least manual effort.
AWS Glue is also the lowest-effort answer because the service is managed and purpose-built for this workflow. The Glue Data Catalog serves as a persistent metadata repository, and AWS documents that crawlers infer schema information and integrate it into the catalog automatically. That means the ML engineer does not need to build custom schema inference logic or manually maintain metadata storage. This is exactly the kind of manual work the question is trying to avoid.
The other options are not as good. SageMaker Data Wrangler is primarily for visual data preparation and feature engineering, not for running a managed ETL-plus-catalog workflow with schema stored in a metadata catalog. Athena with Step Functions would require assembling more custom orchestration and still does not naturally replace the Glue Data Catalog workflow. Launching an EC2 instance introduces the highest operational overhead and does not align with the requirement for least manual effort. Therefore, the best verified AWS-docs answer is A , because AWS Glue combines ETL, schema discovery, and metadata cataloging in one managed service.

NEW QUESTION # 55
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

A. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
B. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
C. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
D. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.

Answer: A

NEW QUESTION # 56
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