Updated Amazon MLA-C01 Exam Questions And Answer

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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 Price:	150 USD
Passing Score:	720 (scaled score 100–1000)
Exam Format:	Multiple choice, Case study, Multiple response, Matching, Ordering
Certificate Validity Period:	3 years
Available Languages:	English, Korean, Japanese, Simplified Chinese
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Real Exam Qty:	65 (50 scored, 15 unscored)
Exam Duration:	130 minutes
Recommended Training:	AWS Certified Machine Learning Engineer - Associate Official Exam Guide AWS Training and Certification
Exam Registration:	AWS Certification Portal Pearson VUE Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE testing centers
Pre Condition:	Recommended: 1+ year hands-on experience with AWS services and machine learning engineering; familiarity with Amazon SageMaker and related ML services. No mandatory prerequisite exams.
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html

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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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	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	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 (Q33-Q38):

NEW QUESTION # 33
A company is building an ML model by using Amazon SageMaker, AWS owned libraries, and open source libraries. The company must ensure that SageMaker does not collect metadata about usage and errors during training. Which solution will meet these requirements?

A. Turn off the setting in the SageMaker domain to share metadata for console jobs. Opt out of metadata collection for each training job that is submitted through the AWS CLI or AWS SDKs.
B. Add an IAM role to the SageMaker domain to deny Amazon CloudWatch the permission to report metadata.
C. Associate the SageMaker domain with a custom IAM role. Attach the role to a policy that denies Amazon CloudWatch service usage logs.
D. Set a parameter to opt out of metadata collection for each training job that is submitted through the AWS CLI, Boto3, or the SageMaker Python SDK.

Answer: A

Explanation:
To prevent metadata collection in Amazon SageMaker, you must disable the metadata sharing setting in the SageMaker domain for console jobs and explicitly opt out of metadata collection for each training job submitted through the AWS CLI or SDKs. This ensures that neither usage nor error metadata is collected during training.

NEW QUESTION # 34
An ML engineer is building an ML model in Amazon SageMaker AI. The ML engineer needs to load historical data directly from Amazon S3, Amazon Athena, and Snowflake into SageMaker AI.
Which solution will meet this requirement?

A. Build a pipeline in SageMaker Pipelines to process the data. Use AWS DataSync to load the processed data into SageMaker AI.
B. Use AWS Glue DataBrew to import the data into SageMaker AI.
C. Use SageMaker Data Wrangler to query and import the data.
D. Create a feature store in SageMaker Feature Store. Use an Apache Spark connector to Feature Store to access the data.

Answer: C

Explanation:
AWS provides Amazon SageMaker Data Wrangler as a native tool for importing, transforming, and analyzing data from multiple sources directly into SageMaker Studio. Data Wrangler supports Amazon S3, Amazon Athena, and Snowflake as built-in data sources through managed connectors.
Using Data Wrangler, ML engineers can query data from Athena using SQL, load structured files from S3, and securely connect to Snowflake without writing custom ingestion code. This approach significantly reduces development effort and aligns with AWS best practices for rapid ML experimentation.
Option A is incorrect because AWS Glue DataBrew is designed for data preparation but does not natively integrate with SageMaker training workflows. Option B introduces unnecessary complexity and is not intended for direct ML data loading. Option C focuses on feature storage, not raw historical data ingestion.
Therefore, SageMaker Data Wrangler is the correct solution.

NEW QUESTION # 35
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 Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 36
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

A. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
B. Initiate a manual Model Monitor job that uses the most recent production data.
C. Adjust the model's parameters and hyperparameters.
D. Include additional data in the existing training set for the model. Retrain and redeploy the model.

Answer: A

NEW QUESTION # 37
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

A. Increase the number of neurons.
B. Investigate and reduce the sources of model bias.
C. Increase the number of layers.
D. Enable early stopping on the model.
E. Increase dropout in the layers.

Answer: D,E

Explanation:
Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.

NEW QUESTION # 38
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