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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q119-Q124):

NEW QUESTION # 119
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?

A. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
B. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
C. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
D. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.

Answer: C

Explanation:
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.

NEW QUESTION # 120
A company is building an enterprise AI platform. The company must catalog models for production, manage model versions, and associate metadata such as training metrics with models. The company needs to eliminate the burden of managing different versions of models.
Which solution will meet these requirements?

A. Use the Amazon SageMaker Model Registry to catalog the models. Create unique tags for each model version. Create key-value pairs to maintain associated metadata.
B. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model. Use the repositories to catalog the models and to manage model versions and associated metadata.
C. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model. Create unique tags for each model version. Create key-value pairs to maintain associated metadata.
D. Use the Amazon SageMaker Model Registry to catalog the models. Create model groups for each model to manage the model versions and to maintain associated metadata.

Answer: D

Explanation:
The correct answer is B. Use the Amazon SageMaker Model Registry to catalog the models. Create model groups for each model to manage the model versions and to maintain associated metadata.
The Amazon SageMaker Model Registry is a managed repository within SageMaker designed specifically for production-grade ML model lifecycle management. It allows organizations to catalog models, track multiple versions of a model, associate rich metadata, and manage deployment workflows in a scalable, controlled manner. Each model can belong to a model group, which acts as a container for all versions of that particular model. Versions can store training metrics, hyperparameters, model artifacts, and other key metadata, enabling reproducibility, auditing, and automated promotion between stages (e.g., Staging # Production).
Option A, while using the Model Registry, relies on manually tagging versions and creating key-value pairs to store metadata. This approach is error-prone, lacks structured versioning, and does not integrate with SageMaker's deployment pipelines.
Options C and D suggest using Amazon ECR repositories. While ECR can store containerized model artifacts, it is not designed for ML-specific metadata, versioning, or automated model stage transitions. Using ECR alone would require custom-built solutions for metadata management, auditing, and version tracking, adding unnecessary operational overhead.
By leveraging the Model Registry with model groups, organizations can automate promotions, apply approval workflows, and track lineage efficiently, fully aligning with AWS best practices for ML model development and production readiness. This ensures compliance, reproducibility, and reduces operational complexity in enterprise AI platforms.
Using the Model Registry and model groups is the standard AWS-recommended approach for enterprise-scale model cataloging and version control, enabling teams to focus on model improvement rather than infrastructure management.

NEW QUESTION # 121
A company wants to share data with a vendor in real time to improve the performance of the vendor's ML models. The vendor needs to ingest the data in a stream. The vendor will use only some of the columns from the streamed data.
Which solution will meet these requirements?

A. Create an Amazon S3 bucket. Configure the S3 bucket policy to allow the vendor to upload data to the S3 bucket. Configure the S3 bucket policy to control which columns are shared.
B. Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink as a consumer to extract relevant columns.
C. Use AWS Lake Formation to ingest the data. Use the column-level filtering feature in Lake Formation to extract relevant columns.
D. Use AWS Data Exchange to stream the data to an Amazon S3 bucket. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) query to define relevant columns.

Answer: B

Explanation:
The requirement specifies real-time streaming ingestion and column-level transformation before sharing data with a vendor. Amazon Kinesis Data Streams is designed for low-latency, real-time data ingestion and delivery.
To extract only required columns from the stream, AWS recommends using Amazon Managed Service for Apache Flink as a stream consumer. Flink enables real-time transformations such as filtering, projection, and enrichment on streaming data before delivering it downstream.
Option A and D are batch-oriented and not suitable for real-time streaming. Option C is incorrect because S3 bucket policies cannot enforce column-level access controls.
Therefore, Kinesis Data Streams combined with Apache Flink meets all requirements.

NEW QUESTION # 122
A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model. How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

A. Configure a real-time inference endpoint.
B. Configure an asynchronous inference endpoint.
C. Configure batch transform.
D. Configure a serverless inference endpoint.

Answer: A

Explanation:
To minimize response-time latency, the ML model should be deployed to a real-time inference endpoint in Amazon SageMaker. This provides low-latency predictions by keeping the model loaded and ready to handle incoming requests, which is critical for an online application serving customers in real time.

NEW QUESTION # 123
A company's dataset for prediction analytics contains duplicate records, missing data, and unusually extreme high or low values. The company needs a solution to resolve the data quality issues quickly. The solution must maintain data integrity and have the LEAST operational overhead.
Which solution will meet these requirements?

A. Use AWS Glue DataBrew to delete duplicate records, fill missing values with medians, and replace extreme values with values in a normal range.
B. Configure an AWS Glue job to identify records with missing values and extreme measurements and delete them.
C. Create an Amazon EMR Spark job to replace missing values with zeros and merge duplicate records.
D. Use Amazon SageMaker Data Wrangler to delete duplicates, apply statistical modeling for missing values, and apply outlier detection algorithms.

Answer: A

Explanation:
AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.
Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.
Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.
AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.
Therefore, Option A is the correct answer.

NEW QUESTION # 124
......

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