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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 Format:	Multiple choice, Multiple response, Matching, Ordering
Exam Duration:	130 minutes
Real Exam Qty:	65 scored questions + 15 unscored questions
Available Languages:	Korean, Simplified Chinese, Japanese, English
Passing Score:	720/1000
Exam Price:	USD 150
Related Certifications:	AWS Certified DevOps Engineer – Professional AWS Certified Data Engineer – Associate AWS Certified AI Practitioner 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	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 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	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 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 (Q46-Q51):

NEW QUESTION # 46
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C. Use AWS Glue DataBrew built-in features to oversample the minority class.
D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Answer: D

NEW QUESTION # 47
A company regularly receives new training data from a 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 AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

A. Use Amazon Managed Workflows for Apache Airflow (MWAA) to orchestrate the pipeline when new data is uploaded.
B. Create an Amazon EventBridge rule that matches S3 upload events and configures the SageMaker pipeline as the target.
C. Create an S3 lifecycle rule to transfer the data to the SageMaker AI training instance and initiate training.
D. Create an AWS Lambda function that scans the S3 bucket and initiates the pipeline when new data is uploaded.

Answer: B

Explanation:
AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.
Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.
Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event- based trigger.
AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 48
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model.
The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
B. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
C. Perform dimensionality reduction on the color categories.
D. Implement padding to ensure that all color feature vectors have the same length.

Answer: B

NEW QUESTION # 49
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data.
Which solution will meet these requirements?

A. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
B. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a recipe job.
C. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.
D. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a profile job.

Answer: C

Explanation:
AWS Glue DataBrew supports datasets sourced directly from Amazon S3 paths. To clean and normalize data, AWS documentation specifies using DataBrew recipes, which define transformation steps such as standardization, deduplication, and formatting.
A profile job is used only for data analysis and statistics generation, not for transformation. A recipe job applies actual transformations to the data and writes the output to Amazon S3.
JDBC connections are used for relational databases, not S3 buckets. Therefore, options C and D are invalid.
AWS clearly documents that the correct workflow is:
* Create a DataBrew dataset from an S3 path
* Define transformations using a recipe
* Run a recipe job to produce cleaned data
Thus, Option B is the correct and AWS-aligned answer.

NEW QUESTION # 50
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

A. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
B. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
C. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
D. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.

Answer: D

Explanation:
Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.

NEW QUESTION # 51
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