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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Machine Learning Engineer - Associate |
| Exam Number: | MLA-C01 |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Certificate Validity Period: | 3 years |
| Exam Format: | Matching, Multiple choice, Multiple response, Case study, Ordering |
| Exam Duration: | 130 minutes |
| Available Languages: | Simplified Chinese, English, Japanese, Korean |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Exam Price: | 150 USD |
| Passing Score: | 720 (scaled score 100–1000) |
| Recommended Training: | AWS Certified Machine Learning Engineer - Associate Official Exam Guide AWS Training and Certification |
| Exam Registration: | Pearson VUE Registration AWS Certification Portal |
| 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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NEW QUESTION # 34
A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
For real-time video content moderation with minimal operational overhead, AWS documentation recommends using fully managed, purpose-built AI services. Amazon Rekognition provides real-time video analysis capabilities, including content moderation, unsafe content detection, and label recognition for live video streams.
By integrating Rekognition with AWS Lambda, the company can automatically process video frames, extract moderation metadata, and take immediate action (such as flagging or stopping a stream) without managing servers, models, or infrastructure. This serverless architecture scales automatically and minimizes operational complexity.
Option B introduces unnecessary complexity. While Amazon Bedrock LLMs are powerful, they are not required for image-based moderation tasks that Rekognition already handles natively.
Option C is incorrect because using Amazon SageMaker would require model training, endpoint management, and scaling, significantly increasing operational overhead.
Option D is incorrect because Amazon Transcribe and Amazon Comprehend are designed for audio and text analysis, not image or video frame moderation.
Therefore, Amazon Rekognition with AWS Lambda is the most efficient, scalable, and low-maintenance solution for real-time video moderation during live streaming events.
NEW QUESTION # 35
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?
Answer: D
Explanation:
The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.
AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.
Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.
Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.
NEW QUESTION # 36
A company collects customer data daily and stores it as compressed files in an Amazon S3 bucket partitioned by date. Each month, analysts process the data, check data quality, and upload results to Amazon QuickSight dashboards.
An ML engineer needs to automatically check data quality before the data is sent to QuickSight, with the LEAST operational overhead.
Which solution will meet these requirements?
Answer: B
Explanation:
AWS Glue Data Quality provides managed, declarative data quality checks with minimal configuration.
Combined with Glue crawlers, it enables automatic schema discovery and quality validation without custom code.
Option A uses native AWS services designed for this exact purpose, minimizing operational overhead.
Options B and C require custom code and maintenance. Option D is not designed for data validation.
AWS documentation explicitly recommends Glue Data Quality rules for scalable, automated data quality checks in analytics pipelines.
Therefore, Option A is the correct and AWS-aligned solution.
NEW QUESTION # 37
A company wants to evaluate a new ML model architecture to understand its performance before deploying the model to production. The company wants to use Amazon SageMaker AI shadow testing.
The company needs to analyze the performance metrics of the shadow model and the production model without affecting the existing production endpoint. The analysis must use real-time inference requests.
Select and order the correct steps to implement shadow testing and compare the model variants in SageMaker AI. Select each step one time or not at all (Select and order Three)
Answer:
Explanation:
Explanation:
Step 1: Update the existing endpoint with a shadow variant. Pick a suitable duration. Start the shadow test.
Step 2: Create a shadow test.
Step 3: Use the SageMaker shadow testing dashboard to analyze the performance differences between the variants.
This is the correct order because SageMaker shadow testing can run against an existing production endpoint.
The production variant continues to receive and respond to inference requests, while the shadow variant receives replicated real-time requests but does not return responses to users. That satisfies the requirement to analyze the new model with real inference traffic without affecting the production endpoint. AWS also states that the SageMaker shadow testing dashboard provides side-by-side comparison metrics for the production and shadow variants, such as latency and error rate.
Do not choose "Create a new endpoint that includes the production model and the shadow model," because the question explicitly says the analysis must not affect the existing production endpoint. Also, do not use the Amazon SageMaker Model Monitoring dashboard here; the specific feature for comparing variants in this scenario is the SageMaker shadow testing dashboard.
NEW QUESTION # 38
A company has historical data that shows whether customers needed long-term support from company staff.
The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?
Answer: D
Explanation:
Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.
NEW QUESTION # 39
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