Amazon MLA-C01 Testing Engine - MLA-C01 Testantworten

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

>> Amazon MLA-C01 Testing Engine <<

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Amazon MLA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.
Thema 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 MLA-C01 Prüfungsfragen mit Lösungen (Q15-Q20):

15. Frage
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::NotebookInstance
B. AWS::SageMaker::Pipeline
C. AWS::SageMaker::Endpoint
D. AWS::SageMaker::Model

Antwort: D

Begründung:
The AWS::SageMaker::Model resource in AWS CloudFormation is used to create an ML model in Amazon SageMaker. This model can then be hosted on an endpoint by using the AWS::SageMaker::Endpoint resource. The model resource defines the container or algorithm to use for hosting and the S3 location of the model artifacts.

16. Frage
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
* Access the store to build datasets for training.
* Create a feature group.
* Ingest the records.

Antwort:

Begründung:

Explanation:

Step 1: Create a feature group.
Step 2: Ingest the records.
Step 3: Access the store to build datasets for training.
Step 1: Create a Feature Group
Why? A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.
How? Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.
Step 2: Ingest the Records
Why? After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.
How? Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.
Step 3: Access the Store to Build Datasets for Training
Why? Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.
How? Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.
Order Summary:
Create a feature group.
Ingest the records.
Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.

17. Frage
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop a chat interface for internal technical documentation.
The documentation consists of dozens of text files totaling several megabytes and is updated frequently.
Which solution will meet these requirements MOST cost-effectively?

A. Fine-tune an LLM in Amazon Bedrock with the documentation.
B. Train a new LLM in Amazon Bedrock using the documentation.
C. Upload the documentation to an Amazon Bedrock knowledge base and use it as context during inference.
D. Use Amazon Bedrock guardrails to integrate documentation.

Antwort: C

Begründung:
AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.
A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.
Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data.
Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.
AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.
Therefore, Option D is the correct and most cost-effective solution.

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

A. 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 AI notebook.
B. Create a lifecycle configuration script to install the custom script when a new SageMaker AI notebook is created. Attach the lifecycle configuration to every new SageMaker AI notebook as part of the creation steps.
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 AI instance. Install the script.
D. Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker AI notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker AI notebook is initialized.

Antwort: B

Begründung:
AWS recommends lifecycle configuration scripts as the simplest and most direct way to customize Amazon SageMaker Notebook Instances at creation time. Lifecycle configurations run automatically when a notebook instance is created or started, allowing scripts, packages, and system dependencies to be installed without manual intervention.
This approach is fully supported, requires no additional infrastructure, and integrates directly with the notebook creation workflow. The script can be reused across notebooks, ensuring consistency.
Options B, C, and D introduce unnecessary complexity, such as container management, private package repositories, or event-driven orchestration.
Therefore, lifecycle configuration scripts provide the least operational overhead solution.

19. Frage
A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained.
Which solution will meet this requirement with the LEAST development effort?

A. Create Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
B. Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.
C. Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
D. Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.

Antwort: B

20. Frage
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