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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q143-Q148):

NEW QUESTION # 143
Hotspot Question
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
- Embedding
- Retrieval Augmented Generation (RAG)
- Temperature
- Token

Answer:

Explanation:


NEW QUESTION # 144
A company needs an AWS solution that will automatically create versions of ML models as the models are created.
Which solution will meet this requirement?

Answer: C


NEW QUESTION # 145
A company needs to combine data from multiple sources. The company must use Amazon Redshift Serverless to query an AWS Glue Data Catalog database and underlying data that is stored in an Amazon S3 bucket.
Select and order the correct steps from the following list to meet these requirements. Select each step one time or not at all. (Select and order three.)
* Attach the IAM role to the Redshift cluster.
* Attach the IAM role to the Redshift namespace.
* Create an external database in Amazon Redshift to point to the Data Catalog schema.
* Create an external schema in Amazon Redshift to point to the Data Catalog database.
* Create an IAM role for Amazon Redshift to use to access only the S3 bucket that contains underlying data.
* Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.

Answer:

Explanation:

Explanation:
Step 1
Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.
This role must include:
Permissions for AWS Glue Data Catalog (e.g., glue:GetDatabase, glue:GetTables) Permissions for the Amazon S3 bucket that stores the underlying data Step 2 Attach the IAM role to the Redshift namespace.
Redshift Serverless uses a namespace, not a cluster, so the role must be associated with the namespace to allow Redshift to assume it when querying external data.
Step 3
Create an external schema in Amazon Redshift to point to the Data Catalog database.
The external schema maps Redshift to the Glue Data Catalog database so Redshift can query the tables stored in S3.


NEW QUESTION # 146
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline ' s correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:

Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.

Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket Why? The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
An S3 event notification invokes the pipeline when new data is uploaded.
SageMaker retrains the model by using the data in the S3 bucket.
The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.


NEW QUESTION # 147
An IoT company uses Amazon SageMaker to train and test an XGBoost model for object detection. ML engineers need to monitor performance metrics when they train the model with variants in hyperparameters. The ML engineers also need to send Short Message Service (SMS) text messages after training is complete.
Which solution will meet these requirements?

Answer: A


NEW QUESTION # 148
......

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