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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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q219-Q224):

NEW QUESTION # 219
A company wants to use Amazon SageMaker to host an ML model that runs on CPU for real-time predictions. The model will have intermittent traffic during business hours and will have periods of no traffic after business hours. The company needs a solution that will serve inference requests in the most cost-effective manner. Which hosting option will meet these requirements?

A. Deploy the model to a SageMaker Serverless Inference endpoint. Configure increased provisioned concurrency during business hours.
B. Deploy the model to a SageMaker real-time endpoint. Create a scheduled AWS Lambda function that activates the endpoint during business hours only.
C. Deploy the model to a SageMaker Asynchronous Inference endpoint. Configure an auto scaling policy that scales in to zero outside business hours.
D. Deploy the model to a SageMaker real-time endpoint. Add a schedule-based auto scaling policy to handle traffic surges during business hours.

Answer: A

Explanation:
SageMaker Serverless Inference is the most cost-effective option for models with intermittent traffic. It automatically scales down to zero when idle, so no cost is incurred outside business hours. Configuring provisioned concurrency during business hours ensures low-latency responses when traffic is expected.

NEW QUESTION # 220
An ML engineer wants to deploy an Amazon SageMaker AI model for inference. The payload sizes are less than 3 MB. Processing time does not exceed 45 seconds. The traffic patterns will be irregular or unpredictable.
Which inference option will meet these requirements MOST cost-effectively?

A. Asynchronous inference
B. Real-time inference
C. Serverless inference
D. Batch transform

Answer: C

Explanation:
Amazon SageMaker Serverless Inference is designed for irregular or unpredictable traffic patterns. It automatically provisions and scales compute resources based on request volume and scales down to zero when idle, making it the most cost-effective option.
Serverless inference supports payloads up to 6 MB and request durations up to 60 seconds, which comfortably meets the stated constraints. Customers are billed only for actual compute usage during inference execution, not for idle capacity.
Asynchronous inference is intended for long-running jobs (up to 1 hour) and large payloads (up to 1 GB).
Real-time inference requires always-on instances, increasing cost during idle periods. Batch transform is designed for offline processing.
Therefore, serverless inference is the optimal choice.

NEW QUESTION # 221
A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features. Which solution will meet these requirements?

A. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
B. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
C. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.
D. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data.Create an Amazon DynamoDB table to store the features.

Answer: C

Explanation:
Amazon SageMaker Feature Processing (via processing jobs) is used to perform feature engineering and data preparation. The engineered features can then be ingested into SageMaker Feature Store, which is a purpose-built service to manage and store ML features for reuse across training and inference. This combination directly addresses the company's requirements.

NEW QUESTION # 222
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 resize image transform. Crop all images to the same size.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform.
Specify the Gamma contrast option.
D. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

Answer: C

NEW QUESTION # 223
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

A. The ML engineer must host the model on AWS Marketplace.
B. The ML engineer must deploy the model to a SageMaker endpoint.
C. The model must be registered in the SageMaker Model Registry.
D. The ML engineer and the Canvas user must be in separate SageMaker domains.
E. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.

Answer: C,E

Explanation:
The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.

NEW QUESTION # 224
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