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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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 (Q229-Q234):

NEW QUESTION # 229
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?

Answer: C


NEW QUESTION # 230
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 # 231
A company is creating an ML model to identify defects in a product. The company has gathered a dataset and has stored the dataset in TIFF format in Amazon S3. The dataset contains 200 images in which the most common defects are visible. The dataset also contains 1,800 images in which there is no defect visible.
An ML engineer trains the model and notices poor performance in some classes. The ML engineer identifies a class imbalance problem in the dataset.
What should the ML engineer do to solve this problem?

Answer: B

Explanation:
Class imbalance occurs when one class significantly outnumbers another, causing models to bias predictions toward the majority class. In this case, images without defects (1,800) vastly outnumber images with defects (200). AWS ML best practices recommend oversampling the minority class to improve class representation without discarding valuable data.
Oversampling techniques-such as duplicating minority samples or applying data augmentation-help the model better learn defect-related features. This approach preserves all available data and improves recall and precision for underrepresented defect classes.
Option B is incorrect because undersampling the minority class would further worsen imbalance. Option A unnecessarily reduces dataset size. Option D does not address the imbalance problem.
Thus, oversampling defect images is the correct solution.


NEW QUESTION # 232
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE- S3).
The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes.
After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors.
What should the ML engineer do to resolve this problem?

Answer: D


NEW QUESTION # 233
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

Answer: C

Explanation:
The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.
Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.
Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.
Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.
Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.


NEW QUESTION # 234
......

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