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Free Amazon AIF-C01 Exam Braindumps (page: 3)

Page 3 of 48
PDF with 233 Questions

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?

  1. Real-time inference
  2. Serverless inference
  3. Asynchronous inference
  4. Batch transform

Answer(s): C

Explanation:

Asynchronous inference is suitable for handling large input data and long processing times while still providing responses without blocking other requests. It allows for near real-time latency, whereas the other options are less suitable given the input size and processing time constraints.



A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

  1. Increase the number of epochs.
  2. Use transfer learning.
  3. Decrease the number of epochs.
  4. Use unsupervised learning.

Answer(s): B

Explanation:

Transfer learning allows the company to adapt pre-trained models for new, related tasks, saving time and resources compared to training models from scratch. The other options do not address the goal of reusing existing models.



A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?

  1. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
  2. Data augmentation by using an Amazon Bedrock knowledge base
  3. Image recognition by using Amazon Rekognition
  4. Data summarization by using Amazon QuickSight Q

Answer(s): A

Explanation:

Human-in-the-loop validation ensures high accuracy by involving human reviewers to verify and correct annotations, minimizing the risk of errors in the generated images. The other options are not directly relevant for ensuring annotation accuracy in image generation.



A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data. Which solution will meet these requirements?

  1. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  2. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  3. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  4. Ensure that the S3 data does not contain sensitive information.

Answer(s): A

Explanation:

The foundation model needs the appropriate permissions to decrypt the encrypted data in the S3 bucket. Ensuring that the role used by Amazon Bedrock has permission to access and decrypt the data will resolve the access failure. The other options are not suitable for addressing the encryption and permission issue.



A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?

  1. Deploy optimized small language models (SLMs) on edge devices.
  2. Deploy optimized large language models (LLMs) on edge devices.
  3. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  4. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer(s): A

Explanation:

Deploying optimized small language models (SLMs) directly on edge devices provides low latency for inference since the computation happens locally, avoiding the delays associated with network communication. The other options either increase latency or are less suitable for edge deployment.



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