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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
Available Languages:	Traditional Chinese, Simplified Chinese, English, Japanese, Korean
Related Certifications:	AWS Certified Data Engineer - Associate AWS Certified Developer - Associate AWS Certified Solutions Architect - Associate
Certificate Validity Period:	3 years
Exam Duration:	170 minutes
Passing Score:	1000-2000 scaled score (passing score approximately 720)
Exam Format:	Multiple choice, Multiple response
Exam Price:	150 USD
Real Exam Qty:	85
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 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 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	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

A. Increase the mini_batch_size hyperparameter.
B. Decrease the dropout rate.
C. Increase the number of epochs.
D. Decrease the early_stopping_patience hyperparameter.

Answer: D

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 # 72
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:

Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.Step 2: SageMaker retrains the model by using the data in the S3 bucket.Step 3: The pipeline deploys the model to a SageMaker endpoint.

* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why?The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How?Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why?The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How?Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why?Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How?Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.

NEW QUESTION # 73
An ML engineer needs to use metrics to assess the quality of a time-series forecasting model.
Which metrics apply to this model? (Choose two.)

A. Recall
B. Average weighted quantile loss (wQL)
C. Root mean square error (RMSE)
D. InferenceLatency
E. LogLoss

Answer: B,C

NEW QUESTION # 74
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
C. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
D. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Answer: D

Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

NEW QUESTION # 75
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?

A. Update the IAM policy that is attached to the execution role for the training jobs. Include the s3:ListBucket and s3:GetObject permissions.
B. Update the S3 bucket policy that is attached to the S3 bucket. Set the value of the aws:SecureTransport condition key to True.
C. Update the IAM policy that is attached to the user that created the training jobs. Include the kms:CreateGrant permission.
D. Update the IAM policy that is attached to the execution role for the training jobs. Include the kms:Encrypt and kms:Decrypt permissions.

Answer: D

NEW QUESTION # 76
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