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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	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 3	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 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q120-Q125):

NEW QUESTION # 120
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 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.
B. 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.
C. 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.
D. 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.

Answer: C

Explanation:
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.

NEW QUESTION # 121
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

A. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
B. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
C. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate.
Use Amazon EventBridge to monitor the data quality and to send alerts.
D. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.

Answer: A

NEW QUESTION # 122
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 TB in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

A. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
B. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
C. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
D. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.

Answer: C

NEW QUESTION # 123
A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.
Which algorithm and hyperparameters should the company use to develop the model?

A. Use the Amazon SageMaker AI XGBoost algorithm. Set the scale_pos_weight hyperparameter to adjust for class imbalance.
B. Use the Amazon SageMaker AI DeepAR algorithm with matching context length and prediction length hyperparameters.
C. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm with contamination to set the expected proportion of anomalies.
D. Use k-means clustering with k to specify the number of clusters.

Answer: B

Explanation:
The problem is a time series forecasting task with historical data and categorical features. Amazon SageMaker DeepAR is purpose-built for this use case. DeepAR uses recurrent neural networks to learn temporal patterns across multiple related time series and supports categorical covariates.
The context length hyperparameter controls how much historical data the model uses as input, while the prediction length specifies how far into the future the model forecasts. Correctly setting these hyperparameters is critical for capturing trends and seasonality.
XGBoost is a general-purpose tabular algorithm and does not model temporal dependencies natively. k-means is a clustering algorithm. Random Cut Forest is used for anomaly detection, not forecasting.
Therefore, DeepAR with appropriate context and prediction lengths is the correct and AWS-recommended solution.

NEW QUESTION # 124
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

A. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
B. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
C. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
D. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.

Answer: A

Explanation:
Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.

NEW QUESTION # 125
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