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Amazon AWS Certified AI Practitioner Sample Questions (Q10-Q15):
NEW QUESTION # 10
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
- A. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
- B. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
- C. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
- D. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
Answer: C
NEW QUESTION # 11
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)
- A. Auto scaling inference endpoints
- B. Data protection
- C. Loosely coupled microservices
- D. Cost optimization
- E. Threat detection
Answer: B,E
Explanation:
To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.
* Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.
* Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.
* Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.
* Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.
* Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.
AWS AI Practitioner References:
* AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.
NEW QUESTION # 12
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?
- A. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
- B. Ensure that the S3 data does not contain sensitive information.
- C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
- D. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
Answer: D
Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
* Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
* Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
* Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
* Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
* Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.
NEW QUESTION # 13
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
- A. Binaries
- B. Models
- C. Embeddings
- D. Tokens
Answer: C
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
* Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
* Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
* Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
* Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
* Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
NEW QUESTION # 14
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?
- A. Topic modeling
- B. Clustering models
- C. BERT-based models
- D. Prescriptive ML models
Answer: C
NEW QUESTION # 15
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