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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: ML Solution Monitoring, Maintenance, and Security	24%	- Monitoring and observability 1. Model drift detection and performance monitoring 2. Infrastructure and data monitoring - Security and governance 1. Compliance and secure ML system design 2. IAM policies and access control
Topic 2: Data Preparation for Machine Learning (ML)	28%	- Data preprocessing and transformation 1. Data validation and quality checks 2. Data cleaning, normalization, and feature engineering - Data ingestion and collection 1. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue) 2. Data acquisition from structured and unstructured sources
Topic 3: Deployment and Orchestration of ML Workflows	22%	- ML pipeline orchestration 1. Workflow automation using AWS services 2. CI/CD pipelines for ML workflows - Model deployment 1. Auto scaling and infrastructure configuration 2. Real-time and batch inference endpoints
Topic 4: ML Model Development	26%	- Model selection and training 1. Training models using Amazon SageMaker 2. Choosing appropriate ML algorithms - Model tuning and evaluation 1. Performance evaluation and metrics interpretation 2. Hyperparameter tuning

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q136-Q141):

NEW QUESTION # 136
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge.
The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

A. Use SageMaker script mode and premade images for ML frameworks.
B. Purchase similar production models through AWS Marketplace.
C. Build a container on AWS that includes custom packages and a choice of ML frameworks.
D. Use SageMaker built-in algorithms to train the proprietary datasets.

Answer: A

NEW QUESTION # 137
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

A. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
B. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
C. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
D. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

Answer: D

NEW QUESTION # 138
An ML engineer has a custom container that performs k-fold cross-validation and logs an average F1 score during training. The ML engineer wants Amazon SageMaker AI Automatic Model Tuning (AMT) to select hyperparameters that maximize the average F1 score.
How should the ML engineer integrate the custom metric into SageMaker AI AMT?

A. Define the average F1 score in the TrainingInputMode parameter.
B. Write the F1 score to a JSON file in Amazon S3 and reference it in ObjectiveMetricName.
C. Publish the average F1 score as a custom Amazon CloudWatch metric.
D. Define a metric definition in the tuning job that uses a regular expression to capture the average F1 score from the training logs.

Answer: D

Explanation:
Amazon SageMaker Automatic Model Tuning extracts objective metrics directly from training logs. For custom containers, AWS requires the ML engineer to define a metric definition that specifies a regular expression to parse the desired metric value from standard output.
The ObjectiveMetricName in the tuning job must match the metric captured by the regex. This is the only supported method for integrating custom metrics with SageMaker AMT.
TrainingInputMode does not define metrics. CloudWatch metrics cannot be used as tuning objectives. AMT cannot read metrics from S3 artifacts.
AWS documentation explicitly states that regex-based metric definitions are required for custom metrics in hyperparameter tuning jobs.
Therefore, Option B is the correct and AWS-verified solution.

NEW QUESTION # 139
An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker. Which solution will provide this functionality with the LEAST implementation effort?

A. Manually install the libraries in the SageMaker containers.
B. Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines.
C. Run code for the libraries externally on Amazon EC2 instances. Store the results in Amazon S3.Import the results into the SageMaker jobs and pipelines.
D. Create a SageMaker notebook instance to host the jobs. Create an AWS Lambda function to install the libraries on the notebook instance when the notebook instance starts. Configure the SageMaker jobs and pipelines to run on the notebook instance.

Answer: B

Explanation:
Building a custom Docker container with the required libraries and hosting it in Amazon ECR allows SageMaker jobs, training, and pipelines to consistently use the same environment. This approach minimizes manual setup, ensures portability, and provides the least ongoing implementation effort compared to repeatedly installing or managing libraries separately.

NEW QUESTION # 140
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 model groups for each model to manage the model versions and 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. 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.
D. 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.

Answer: A

Explanation:
AWS enterprise ML best practices recommend using Amazon SageMaker Model Registry to manage models throughout their lifecycle. The Model Registry is designed specifically to catalog models, track versions, and associate metadata such as training metrics, approval status, and deployment history.
Model Registry introduces the concept of model groups, which act as logical containers for different versions of the same model. Each model version within a group automatically inherits versioning, metadata tracking, and governance controls. This eliminates the operational burden of manually managing model versions and ensures consistent lineage and traceability across development, testing, and production environments.
Option A is less optimal because manually tagging model versions increases operational complexity and does not take full advantage of the built-in version management features provided by model groups.
Options C and D are incorrect because Amazon ECR is a container image repository, not a model governance or lifecycle management service. Using ECR to manage ML model versions would require custom tooling and manual metadata handling, significantly increasing operational overhead.
By using model groups within SageMaker Model Registry, the company gains a centralized, scalable, and AWS-native solution for enterprise AI governance. This approach directly aligns with AWS documentation for managing model catalogs, version control, and metadata association while minimizing manual intervention.

NEW QUESTION # 141
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