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

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q54-Q59):

NEW QUESTION # 54
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 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.
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 shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
D. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.

Answer: A

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 # 55
A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result.
The models should be evaluated based on the following criteria:
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs
After creating each binary classification model, the data scientist generates the corresponding confusion matrix.
Which confusion matrix represents the model that satisfies the requirements?

A. TN = 96, FP = 4
FN = 10, TP = 90
B. TN = 91, FP = 9
FN = 22, TP = 78
C. TN = 98, FP = 2
FN = 18, TP = 82
D. TN = 99, FP = 1
FN = 21, TP = 79

Answer: C

Explanation:
The following calculations are required:
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
FN = False Negative
Recall = TP / (TP + FN)
False Positive Rate (FPR) = FP / (FP + TN)
Cost = 5 * FP + FN

NEW QUESTION # 56
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

A. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re- training job.
B. Use Amazon Quick Suite (previously known as Amazon QuickSight) anomaly detection to detect data drift. Use an AWS Step Functions workflow to automate the re-training job.
C. Use Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda function to automate the re-training job.
D. Use an AWS Glue crawler and an AWS Glue extract, transform, and load (ETL) job to detect data drift.
Use AWS Glue triggers to automate the retraining job.

Answer: A

Explanation:
AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.
When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.
Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.
Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.
Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.
Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.

NEW QUESTION # 57
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

A. Recreate the training dataset by using the Data Wrangler enhance image contrast transform.
Specify the Gamma contrast option.
B. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
C. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
D. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

Answer: A

NEW QUESTION # 58
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

A. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
B. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
C. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group.Associate the group with each notebook instance that the team uses.
D. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user.
Configure each notebook instance to use the IAM user.

Answer: B

NEW QUESTION # 59
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