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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Available Languages: | Japanese, Portuguese (Brazil), English, Korean, Simplified Chinese |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning Engineer - Associate AWS Certified Data Engineer - Associate |
| Real Exam Qty: | 80 |
| Exam Format: | Multiple response, Multiple choice |
| Exam Price: | USD 100 |
| Passing Score: | 700 / 1000 |
| Certificate Validity Period: | 3 years |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 430
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
NEW QUESTION # 431
An AI practitioner wants to perform a contextual grounding check in Amazon Bedrock.
Which three components must the AI practitioner use?
Answer: B
Explanation:
Amazon Bedrock Guardrails provides contextual grounding checks to determine whether foundation model responses are supported by reference information and relevant to user queries.
According to AWS documentation, contextual grounding checks require three components:
1. Grounding source: Reference information used to establish factual context.
2. User query: The question or request submitted by the user.
3. Model response: The generated content evaluated against the reference source and query.
These checks evaluate two important dimensions: grounding, which determines whether claims are supported by source information, and relevance, which determines whether responses address the user ' s question.
Option B includes API credentials, which are not a contextual grounding component. Option C describes development interfaces. Option D includes confidence scores and thresholds, which support evaluation but cannot replace the required source and response.
Therefore, option A is correct.
AWS Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-contextual-grounding- check.html
NEW QUESTION # 432
A company wants to use an AI/ML service to build a conversational AI application.
Which AWS service will meet these requirements?
Answer: A
Explanation:
Amazon Lex is the AWS service purpose-built for creating conversational interfaces and chatbots that interact with users through text or speech.
AWS defines Amazon Lex V2 as "an AWS service for building conversational interfaces for applications using voice and text." It provides natural language understanding (NLU) and automatic speech recognition (ASR), enabling developers to create conversational applications without needing to implement the underlying speech-recognition and language-understanding models themselves.
A typical Amazon Lex application defines intents, utterances, slots, prompts, and conversational flows. Lex identifies what the user is attempting to accomplish, collects required information, manages dialogue state, and can integrate with backend systems through services such as AWS Lambda. It can therefore power customer-service bots, self-service assistants, booking systems, help-desk interfaces, and other conversational applications.
The other services address narrower AI capabilities.
Amazon Comprehend is primarily a natural language processing service for deriving insights from text, including sentiment, entities, key phrases, language identification, classification, and related NLP tasks. It is not the primary service for managing interactive conversational dialogue.
Amazon Polly converts text into lifelike speech. Polly can be incorporated into voice applications, but text-to- speech generation alone does not provide intent recognition, dialogue management, or conversational understanding.
Amazon Translate performs neural machine translation between supported languages. It can help multilingual applications but does not implement a conversational interface.
Amazon Lex combines speech recognition, language understanding, intent detection, slot collection, and dialogue management within a managed service. AWS further notes that developers can build, test, and publish both text and voice chatbots through Lex.
Therefore, for an application whose fundamental requirement is conversational AI , the AWS service that directly satisfies the requirement is B. Amazon Lex .
NEW QUESTION # 433
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
Answer: A
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
NEW QUESTION # 434
A company is developing a customer service agent by using Amazon Bedrock. The company wants to ensure that the agent does not disclose personally identifiable information (PII) during conversations with users.
Which Amazon Bedrock feature meets these requirements?
Answer: B
Explanation:
The verified answer is A. Amazon Bedrock Guardrails. AWS documentation states that Amazon Bedrock Guardrails helps detect sensitive information, including personally identifiable information, in input prompts or model responses by using sensitive information filters. AWS also states that these filters can be configured to block or mask detected PII. This directly matches the requirement to prevent a customer service agent from disclosing PII during conversations.
Bedrock Guardrails supports handling sensitive information in two key ways. A block action can block requests or responses that include sensitive information, and a mask action can anonymize or redact detected information in model requests or responses. AWS gives examples of PII categories such as names, email addresses, phone numbers, usernames, passwords, addresses, credit card numbers, PINs, and other sensitive identifiers. For a customer service agent, this is exactly the right feature because PII could appear in either the user input or the model response.
Amazon Bedrock Flows is incorrect because flows help orchestrate generative AI workflows. They do not primarily provide PII filtering. Amazon Bedrock Knowledge Bases is incorrect because knowledge bases support Retrieval Augmented Generation by connecting models to enterprise data sources. They help ground responses but do not directly block PII disclosure. Amazon Bedrock Data Automation is incorrect because BDA is used to transform and extract insights from multimodal content such as documents, images, audio, and video. It is not the runtime guardrail feature for blocking or masking PII in conversations.
Therefore, the correct Amazon Bedrock feature is Amazon Bedrock Guardrails.
NEW QUESTION # 435
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