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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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 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	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 (Q195-Q200):

NEW QUESTION # 195
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. 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.
B. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 196
An ML engineer wants to run a training job on Amazon SageMaker AI by using multiple GPUs. The training dataset is stored in Apache Parquet format.
The Parquet files are too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?

A. Repartition the Parquet files by using Apache Spark on Amazon EMR and use the repartitioned files for training.
B. Use SageMaker distributed data parallelism (SMDDP) to split memory usage.
C. Attach an Amazon EBS Provisioned IOPS SSD volume and store the files on the EBS volume.
D. Change to memory-optimized instance types with sufficient memory.

Answer: A

Explanation:
Large Parquet files can cause out-of-memory (OOM) issues during training if individual files exceed the memory capacity of the training instances. AWS documentation recommends repartitioning large datasets into smaller files to enable efficient streaming and parallel loading.
By using Apache Spark on Amazon EMR to repartition the Parquet files, the ML engineer can split the dataset into multiple smaller files that can be read incrementally during training. This approach avoids loading a single large file into memory and improves data parallelism.
Attaching larger EBS volumes increases storage capacity but does not solve memory constraints. Switching to memory-optimized instances increases cost and is not necessary when the dataset can be restructured.
SageMaker distributed data parallelism focuses on model parameter synchronization across GPUs, not dataset file size.
AWS best practices explicitly recommend partitioning large Parquet datasets to improve memory efficiency during training.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 197
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
B. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
C. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.
D. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

Answer: B

NEW QUESTION # 198
A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio.
After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .
Check the analysis results on the SageMaker Studio console. .
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Schedule an hourly model explainability monitor.

Answer:

Explanation:

Explanation:
Step 1:
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Step 2:
Schedule an hourly model explainability monitor.
Step 3:
Check the analysis results on the SageMaker Studio console.
When monitoring a deployed model for data drift and explainability, AWS prescribes a specific workflow using SageMaker Clarify and SageMaker Model Monitor:
Create a SHAP baseline (Step 1)Before any monitoring can occur, SageMaker Clarify must establish a baseline explainability configuration. This baseline captures the reference SHAP values for feature importance using training or baseline data. Model Monitor uses this baseline to compare future inferences and detect drift in feature attributions.
Schedule the model explainability monitor (Step 2)After the baseline is created, an explainability monitoring schedule must be configured (hourly in this case). The monitor periodically analyzes inference data, compares it against the SHAP baseline, and generates reports that highlight drift or anomalies in feature contributions.
Inspect results in SageMaker Studio (Step 3)Once monitoring jobs run, SageMaker stores the analysis results in Amazon S3 and surfaces them in the SageMaker Studio console, where engineers can review metrics, violations, and visual reports.
This sequence is mandatory because:
A monitor cannot run without a baseline
Results cannot be reviewed until the monitor executes

NEW QUESTION # 199
A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always be up to date with the latest changes in the source systems.
Which solution will meet these requirements?

A. Use cataloged connections to import data from the data sources into Data Wrangler.
B. Use AWS Glue to extract data from the data sources. Use AWS Glue also to import the data directly into Data Wrangler.
C. Use AWS Lambda to extract data from the data sources. Use Lambda also to import the data directly into Data Wrangler.
D. Use direct connections to import data from the data sources into Data Wrangler.

Answer: A

Explanation:
Amazon SageMaker Data Wrangler supports both direct and cataloged connections. To ensure data is always up to date, AWS recommends using cataloged connections backed by AWS Glue Data Catalog.
Cataloged connections allow Data Wrangler to reference the source systems dynamically, ensuring that each import reflects the latest data changes without manual reconfiguration. This approach supports Amazon S3, Amazon Redshift, and Snowflake and integrates securely using managed credentials.
Direct connections are point-in-time imports and do not automatically reflect schema or data updates. Glue and Lambda-based solutions introduce unnecessary complexity and operational overhead.
Therefore, using cataloged connections in Data Wrangler is the correct solution.

NEW QUESTION # 200
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