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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 (Q156-Q161):

NEW QUESTION # 156
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset.
The model must make continuous numeric predictions based on 10 years of data.
Which metric should the ML engineer use to evaluate the model's performance?

Answer: D

Explanation:
This is a regression problem, where the target variable is continuous and numeric. AWS documentation clearly states that classification metrics such as accuracy and AUC are not appropriate for regression models.
Root Mean Square Error (RMSE) measures the square root of the average squared differences between predicted and actual values. RMSE penalizes larger errors more heavily, making it especially useful when large prediction errors are costly or undesirable.
SageMaker Canvas automatically selects regression metrics such as RMSE and MAE when building regression models. RMSE is widely used for time-based and numeric prediction problems, especially when evaluating long historical datasets.
Inference latency measures system performance, not model accuracy.
Therefore, Option D is the correct and AWS-verified answer.


NEW QUESTION # 157
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?

Answer: B

Explanation:
Apache Parquet is a columnar storage file format optimized for complex and large datasets. It provides efficient reading and processing by accessing only the required columns, which reduces I/O and speeds up data handling. This makes it ideal for use with Amazon SageMaker Canvas, where minimizing processing time is important for training ML models. Parquet is also compatible with S3 and widely supported in data analytics and ML workflows.


NEW QUESTION # 158
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

Answer: D


NEW QUESTION # 159
A company uses the Amazon SageMaker AI Object2Vec algorithm to train an ML model. The model performs well on training data but underperforms after deployment. The company wants to avoid overfitting the model and maintain the model's ability to generalize.
Which solution will meet these requirements?

Answer: C

Explanation:
The described behavior-strong performance on training data but poor performance in production-is a classic sign of overfitting. AWS documentation for Amazon SageMaker Object2Vec highlights early stopping as a key regularization technique to prevent models from learning noise in the training data.
The early_stopping_patience hyperparameter controls how many additional epochs the training job will run after the validation loss stops improving. Decreasing this value causes training to stop earlier, reducing the chance that the model overfits to the training dataset.
Option B increases batch size, which may improve training efficiency but does not directly address overfitting. Option C decreases the dropout rate, which actually increases overfitting risk, since dropout is a regularization mechanism. Option D increases epochs, which further worsens overfitting.
AWS best practices emphasize early stopping combined with validation metrics as one of the most effective ways to maintain generalization performance in neural embedding models such as Object2Vec.
Therefore, Option A is the correct and AWS-aligned solution.


NEW QUESTION # 160
A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

Answer: D

Explanation:
AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.
Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.
Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution. Option B introduces custom code and periodic scanning, which increases operational complexity and cost. Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event- based trigger.
AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.
Therefore, Option C is the correct and AWS-verified answer.


NEW QUESTION # 161
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