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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	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 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	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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q213-Q218):

NEW QUESTION # 213
A travel company has trained hundreds of geographic data models to answer customer questions by using Amazon SageMaker AI. Each model uses its own inferencing endpoint, which has become an operational challenge for the company.
The company wants to consolidate the models' inferencing endpoints to reduce operational overhead.
Which solution will meet these requirements?

A. Use SageMaker AI multi-model endpoints. Deploy a single endpoint.
B. Use Amazon SageMaker Studio. Deploy a single-model endpoint.
C. Use SageMaker AI multi-container endpoints. Deploy a single endpoint.
D. Use inference pipelines in SageMaker AI to combine tasks from hundreds of models to 15 models.

Answer: A

Explanation:
AWS provides Amazon SageMaker Multi-Model Endpoints to host multiple trained models behind a single inference endpoint. This feature dynamically loads models into memory based on request traffic, significantly reducing endpoint sprawl and operational overhead.
Multi-model endpoints are ideal when an organization has hundreds or thousands of related models, such as geographic or regional models, that are not all invoked simultaneously. AWS documentation highlights this approach as a best practice for cost optimization and simplified deployment management.
Option B (multi-container endpoints) is intended for chaining a small number of models, not hundreds.
Option C does not address endpoint consolidation. Option D changes the modeling strategy and is not required.
Therefore, using a single SageMaker multi-model endpoint is the correct solution.

NEW QUESTION # 214
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

A. Create a model group for each category. Move the existing models into these category model groups.
B. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
C. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
D. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

Answer: C

NEW QUESTION # 215
A company must install a custom script on any newly created Amazon SageMaker notebook instances.
Which solution will meet this requirement with the LEAST operational overhead?

A. Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.
B. Create a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
C. Create a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
D. Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.

Answer: D

NEW QUESTION # 216
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 a JDBC driver to connect to the S3 bucket. Use a recipe job.
B. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a profile job.
C. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
D. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.

Answer: D

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 # 217
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development.
An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
B. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
D. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.

Answer: D

Explanation:
Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.

NEW QUESTION # 218
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