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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer - Associate
Exam Number:	MLA-C01
Exam Format:	Multiple choice, Multiple response
Related Certifications:	AWS Certified Developer - Associate AWS Certified Solutions Architect - Associate AWS Certified Data Engineer - Associate
Exam Price:	150 USD
Real Exam Qty:	85
Certificate Validity Period:	3 years
Passing Score:	1000-2000 scaled score (passing score approximately 720)
Exam Duration:	170 minutes
Available Languages:	Japanese, English, Korean, Traditional Chinese, Simplified Chinese
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored (Pearson VUE) or in-person testing center
Pre Condition:	Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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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	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	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q141-Q146):

NEW QUESTION # 141
A company is building a near real-time data analytics application to detect anomalies and failures for industrial equipment. The company has thousands of IoT sensors that send data every 60 seconds. When new versions of the application are released, the company wants to ensure that application code bugs do not prevent the application from running.
Which solution will meet these requirements?

A. Use Amazon Managed Service for Apache Flink with the system rollback capability enabled to build the data analytics application.
B. Use Amazon Managed Service for Apache Flink with manual rollback when an error occurs to build the data analytics application.
C. Use Amazon Data Firehose to deliver real-time streaming data programmatically for the data analytics application. Pause the stream when a new version of the application is released and resume the stream after the application is deployed.
D. Use Amazon Data Firehose to deliver data to Amazon EC2 instances across two Availability Zones for the data analytics application.

Answer: A

Explanation:
For near real-time anomaly detection on streaming IoT data, AWS recommends Amazon Managed Service for Apache Flink. Flink is designed for stateful, low-latency stream processing and is well suited for time- series sensor analytics.
A key requirement is application resilience during deployments. Managed Flink supports system rollback, which automatically reverts to the last stable application version if a new deployment fails. This capability ensures uninterrupted processing even if application bugs are introduced, meeting the company's reliability requirement.
Manual rollback (Option B) introduces operational risk and delays. Amazon Data Firehose (Options C and D) is a delivery service and does not support complex anomaly detection logic or application version rollback.
Therefore, using Managed Flink with system rollback enabled is the correct solution.

NEW QUESTION # 142
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.
Which solution will meet these requirements?

A. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
B. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
C. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.
D. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.

Answer: C

Explanation:
AWS best practices for cost governance recommend using resource tagging combined with AWS Budgets to track and alert on service-level spending. By adding tags at the SageMaker Studio user profile level, all compute resources launched by users inherit those tags automatically.
AWS Budgets supports threshold-based alerts, unlike AWS Cost Explorer, which is primarily used for historical analysis and visualization. Budgets can trigger notifications via email or Amazon SNS when spending exceeds defined limits.
IAM profiles are unrelated to cost tracking, making options C and D invalid. Therefore, tagging SageMaker user profiles and using AWS Budgets is the correct solution.

NEW QUESTION # 143
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models.
Which solution will meet these requirements?

A. Create an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker multi-model endpoint. Point the endpoint to the S3 bucket. Create an auto scaling policy for the endpoint. Set the parameter for the minimum number of copies to at least 1.
B. Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1.
C. Create a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling policy for each endpoint.
D. Create a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency for the endpoints.

Answer: B

NEW QUESTION # 144
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow.
The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
B. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
C. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
D. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.

Answer: B

NEW QUESTION # 145
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?

A. Oversample the 200 images in which the most common defects are visible.
B. Use all 2,000 images and Amazon Rekognition Custom Labels to train a new model.
C. Use a few hundred images and Amazon Rekognition Custom Labels to train a new model.
D. Undersample the 200 images in which the most common defects are visible.

Answer: A

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 # 146
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