RELIABLE MLA-C01 DUMPS, VALID STUDY MLA-C01 QUESTIONS

Reliable MLA-C01 Dumps, Valid Study MLA-C01 Questions

Reliable MLA-C01 Dumps, Valid Study MLA-C01 Questions

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q56-Q61):

NEW QUESTION # 56
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

  • A. Decrease the temperature parameter. Increase the top_k parameter.
  • B. Increase the temperature parameter. Decrease the top_k parameter.
  • C. Increase the temperature parameter and the top_k parameter.
  • D. Decrease the temperature parameter and the top_k parameter.

Answer: D

Explanation:
Thetemperatureparameter controls the randomness in the model's responses. Lowering the temperature makes the model produce more deterministic and consistent answers.
Thetop_kparameter limits the number of tokens considered for generating the next word. Reducing top_k further constrains the model's options, ensuring more predictable responses.
By decreasing both parameters, the responses become more focused and consistent, reducing variability in similar queries.


NEW QUESTION # 57
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

  • A. Build a container on AWS that includes custom packages and a choice of ML frameworks.
  • B. Use SageMaker script mode and premade images for ML frameworks.
  • C. Purchase similar production models through AWS Marketplace.
  • D. Use SageMaker built-in algorithms to train the proprietary datasets.

Answer: B

Explanation:
SageMaker script mode allows you to bring existing custom Python scripts and run them on AWS with minimal changes. SageMaker provides prebuilt containers for ML frameworks like PyTorch, simplifying the migration process. This approach enables the company to leverage their existing Python scripts and domain knowledge while benefiting from the scalability and managed environment of SageMaker. It requires the least effort compared to building custom containers or retraining models from scratch.


NEW QUESTION # 58
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

  • A. Set up SageMaker Debugger and create a custom rule.
  • B. Set up blue/green deployments with canary traffic shifting.
  • C. Set up shadow testing with a shadow variant of the new model.
  • D. Set up blue/green deployments with all-at-once traffic shifting.

Answer: C

Explanation:
Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.


NEW QUESTION # 59
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

  • A. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
  • B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
  • C. Apply statistics from a well-known dataset to normalize the production samples.
  • D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

Answer: B

Explanation:
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.


NEW QUESTION # 60
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?

  • A. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  • B. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  • C. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
  • D. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

Answer: D

Explanation:
SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.
SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.


NEW QUESTION # 61
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