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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
  • 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 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q116-Q121):

NEW QUESTION # 116
Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?

Answer: A


NEW QUESTION # 117
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible.
Which SageMaker AI feature should the company use?

Answer: A

Explanation:
SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.
With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.
Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.
Therefore, Option D is the correct and AWS-recommended choice.


NEW QUESTION # 118
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.

Answer:

Explanation:

Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why?The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How?Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why?Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How?Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why?After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How?Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.


NEW QUESTION # 119
A company is training a deep learning model to detect abnormalities in images. The company has limited GPU resources and a large hyperparameter space to explore. The company needs to test different configurations and avoid wasting computation time on poorly performing models that show weak validation accuracy in early epochs.
Which hyperparameter optimization strategy should the company use?

Answer: C

Explanation:
When GPU resources are limited and the hyperparameter search space is large, AWS documentation strongly recommends Bayesian optimization combined with early stopping. Bayesian optimization uses past evaluation results to intelligently select the next set of hyperparameters to test, focusing exploration on promising regions of the search space rather than testing all combinations.
In Amazon SageMaker, Bayesian optimization is the default and recommended strategy for hyperparameter tuning jobs. It significantly reduces the number of training runs required compared to grid or random search, making it highly cost-efficient for deep learning workloads.
Early stopping further improves efficiency by terminating training jobs that show poor validation performance in early epochs. This prevents wasted GPU time on configurations that are unlikely to perform well. AWS explicitly documents early stopping as a key feature for controlling training cost and duration.
Grid search and exhaustive search are computationally expensive and impractical for large hyperparameter spaces. Manual tuning is slow, error-prone, and does not scale.
By combining Bayesian optimization with early stopping, the company can rapidly converge on high- performing hyperparameter configurations while minimizing resource usage.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 120
A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?

Answer: B

Explanation:
Amazon SageMaker supports direct file system access for training jobs through Amazon FSx. AWS documentation states that FSx for NetApp ONTAP file systems can be mounted directly to SageMaker training instances when they are located in the same VPC.
Mounting the FSx file system allows SageMaker to stream large datasets efficiently without copying data into Amazon S3. This is ideal for very large datasets such as 6 TB of image data and avoids unnecessary storage duplication.
Options involving SageMaker Data Wrangler are intended for data preparation and exploration, not large- scale training data access. Mountpoint for Amazon S3 is not required and introduces additional complexity.
Therefore, Option A is the correct and AWS-aligned solution.


NEW QUESTION # 121
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