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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 80 |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700 / 1000 |
| Exam Format: | Multiple choice, Multiple response |
| Related Certifications: | AWS Certified Data Engineer - Associate AWS Certified Cloud Practitioner AWS Certified Machine Learning Engineer - Associate |
| Exam Price: | USD 100 |
| Available Languages: | Korean, Japanese, English, Simplified Chinese, Portuguese (Brazil) |
| 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 # 22
Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all. (Select THREE.)
* Chain-of-thought prompting
* Few-shot prompting
* Role-based prompting
* Single-shot prompting
* Zero-shot prompting
Answer:
Explanation:
The verified selections are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting. The first description matches few-shot prompting because AWS describes few-shot prompting as a technique that includes example outputs or demonstrations in the initial prompt so the model can understand the expected pattern before generating a response. The phrase "provide a small number of examples" is the key indicator.
A few-shot prompt gives the model limited examples of the desired task, format, or reasoning style, and the model uses those examples as context for the next output.
The second description matches chain-of-thought prompting. AWS describes chain-of-thought prompting as a technique that helps a model solve a problem by following a series of intermediate reasoning steps before reaching the final answer. The wording "break down the step-by-step process" directly points to chain-of- thought prompting. This method is commonly associated with reasoning, arithmetic, logic, planning, and multi-step problem solving because the prompt encourages the model to work through intermediate steps rather than immediately outputting a final answer.
The third description matches zero-shot prompting because AWS describes zero-shot prompting as asking the model to perform a task without providing examples in the prompt. The model relies only on the instruction and its pre-trained knowledge. The phrase "without providing examples" is the decisive clue.
Role-based prompting is not used here because none of the descriptions asks the model to act as a specific persona, job role, or domain expert, such as "Act as a financial analyst" or "You are a security engineer." Single-shot prompting is also not used because the first description says a "small number of examples," which indicates few-shot prompting, not exactly one example. Therefore, the three correct hotspot mappings are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting.
NEW QUESTION # 23
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:
Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
NEW QUESTION # 24
Which AWS service helps select foundation models (FMs) for generative AI use cases?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock provides access to multiple foundation models from different providers and enables customers to evaluate, compare, and select the most appropriate model for their generative AI use cases.
Amazon Bedrock:
* Offers a choice of foundation models
* Supports model evaluation and customization
* Abstracts infrastructure management
Why the other options are incorrect:
* Amazon Personalize (A) is a recommendation service.
* Amazon Q Developer (C) is a coding assistant.
* Amazon Rekognition (D) is an image and video analysis service.
AWS AI document references:
* Amazon Bedrock Overview
* Choosing Foundation Models on AWS
* Generative AI Model Selection Guidance
NEW QUESTION # 25
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide.
The employees have minimal experience with technology.
Which solution meets these requirements?
Answer: C
Explanation:
Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.
* GPT for Natural Language Processing:
* GPT models are designed for understanding and generating human-like text based on natural language input.
* They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.
* Why Option A is Correct:
* Natural Language Understanding: GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.
* User-Friendly: Requires minimal technology experience from employees, as they provide simple text input.
* Why Other Options are Incorrect:
* B. Residual neural network: Typically used in computer vision tasks, not for natural language- to-SQL conversion.
* C. Support vector machine: Used for classification tasks, not for generating structured queries from text.
* D. WaveNet: A deep generative model for audio data, unrelated to text-to-SQL tasks.
NEW QUESTION # 26
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 # 27
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