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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
Available Languages:	Japanese, Korean, English, Simplified Chinese
Exam Price:	USD 150
Passing Score:	720/1000
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Duration:	130 minutes
Certificate Validity Period:	3 years
Exam Format:	Multiple choice, Matching, Ordering, Multiple response
Related Certifications:	AWS Certified DevOps Engineer – Professional AWS Certified AI Practitioner AWS Certified Data Engineer – Associate 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 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 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	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	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q20-Q25):

NEW QUESTION # 20
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

A. Set up blue/green deployments with all-at-once traffic shifting.
B. Set up blue/green deployments with canary traffic shifting.
C. Set up shadow testing with a shadow variant of the new model.
D. Set up SageMaker Debugger and create a custom rule.

Answer: C

Explanation:
Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.

NEW QUESTION # 21
A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock.
The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have a security group that allows access to all IP addresses in the private subnet.
What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

A. Use AWS Direct Connect to link the VPC to Amazon Bedrock.
B. Configure Amazon Bedrock to use the private subnet where the EC2 instances are deployed.
C. Use AWS PrivateLink to access Amazon Bedrock through an interface VPC endpoint.
D. Modify the security group to allow inbound and outbound traffic to and from Amazon Bedrock.

Answer: C

Explanation:
Amazon Bedrock is a fully managed AWS service and does not reside inside customer VPCs. To allow private connectivity from EC2 instances in a private subnet without using the public internet, AWS documentation recommends AWS PrivateLink.
By creating an interface VPC endpoint for Amazon Bedrock, traffic between the EC2 instances and Bedrock remains entirely within the AWS network. This approach preserves the private subnet configuration and meets strict security requirements.
Security groups alone cannot establish connectivity to a managed service endpoint. Amazon Bedrock cannot be deployed into a customer subnet, making Option C invalid. AWS Direct Connect is designed for on- premises connectivity, not for accessing AWS managed services from within a VPC.
AWS explicitly documents that PrivateLink is the correct mechanism for private access to Amazon Bedrock.
Therefore, Option B is the correct and AWS-verified answer.

NEW QUESTION # 22
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero.
B. Increase the value of the target_precision hyperparameter.
C. Increase the number of training epochs.
D. Change the value of the predictorjype hyperparameter to regressor.

Answer: B

NEW QUESTION # 23
A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.
Which solution will optimize the scaling process without affecting response times?

A. Increase the cooldown period after scale-out activities.
B. Change to a multi-model endpoint configuration.
C. Decrease the scale-in cooldown period and increase the maximum instance count.
D. Integrate Amazon API Gateway and AWS Lambda to manage invocations.

Answer: A

Explanation:
Amazon SageMaker auto scaling uses cooldown periods to control how frequently scaling activities occur.
When scale-out happens too quickly, new instances may receive traffic before they are fully initialized, leading to inefficiencies and latency.
AWS documentation recommends increasing the scale-out cooldown period to give newly launched instances sufficient time to initialize and become healthy before additional scaling events occur. This ensures stable performance during traffic spikes without impacting response times.
Multi-model endpoints address model hosting efficiency, not scaling timing. API Gateway and Lambda add unnecessary latency and complexity. Decreasing scale-in cooldown does not address scale-out issues.
Therefore, Option D is the correct and AWS-aligned solution.

NEW QUESTION # 24
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.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?

A. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
B. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
C. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
D. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Answer: A

Explanation:
Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on-premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
Analyze and Visualize - Amazon SageMaker
Transform Data - Amazon SageMaker

NEW QUESTION # 25
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