AWS Certified AI Practitioner AIF-C01 Exam Dumps

In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.

Option B (Correct): "Hallucination":This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.

Option A:"Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.

Option C:"Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.

Option D:"Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.

AWS AI Practitioner References:

Large Language Models on AWS:AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.

AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.

Option D (Correct): "AWS Data Exchange":This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.

Option A:"AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.

Option B:"AWS Artifact" is incorrect as it provides access to AWS’s compliance reports, not ISVs'.

Option C:"AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.

AWS AI Practitioner References:

AWS Data Exchange Documentation:AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.

To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.

Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference":This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.

Option A:"Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.

Option C:"Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.

Option D:"Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.

AWS AI Practitioner References:

Amazon SageMaker Endpoints:AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.

Adjust the Prompt:

Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.

A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish."

Why Option A is Correct:

Control Over Output:Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.

Flexibility:Prompt engineering is a flexible approach to refining the model’s behavior without modifying the model itself.

Why Other Options are Incorrect:

B. Choose an LLM of a different size:The model size does not directly impact the response length or language.

C. Increase the temperature:Increases randomness in responses but does not ensure brevity or specific language.

D. Increase the Top K value:Affects diversity in model output but does not align directly with response length or language specificity.