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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Exam Price: | 100 USD |
| Passing Score: | 700 (scaled score 100โ1000) |
| Related Certifications: | AWS Certified Machine Learning โ Specialty AWS Certified Cloud Practitioner |
| Available Languages: | English, Traditional Chinese, Japanese, Korean, Simplified Chinese |
| Exam Format: | Multiple choice, Ordering, Multiple response |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Duration: | 90 minutes |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
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NEW QUESTION # 314
A company wants to use foundation models (FMs) to develop and deploy an AI model.
Which AWS service or resource will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock is the managed AWS service designed specifically to develop, customize, and deploy foundation models with minimal effort.
Amazon Bedrock:
* Provides direct access to multiple foundation models
* Eliminates infrastructure management
* Supports customization, evaluation, and deployment
Why the other options are incorrect:
* SageMaker AI (B) requires more setup and ML expertise.
* PartyRock (C) is for experimentation, not deployment.
* Amazon Q Developer (D) is a productivity assistant.
AWS AI document references:
* Amazon Bedrock Overview
* Foundation Model Deployment on AWS
* Reducing Development Effort with Managed AI Services
NEW QUESTION # 315
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
Answer: A
Explanation:
* Retraining or pre-training is costly and unnecessary for just using company policies.
* Fine-tuning adapts models but is inefficient for frequently changing company documents.
* Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
# Reference:
AWS Documentation - RAG with Amazon Bedrock
NEW QUESTION # 316
Which task describes a use case for intelligent document processing (IDP)?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From AWS AI documents (with references):
Intelligent Document Processing (IDP) on AWS is focused on extracting structured information from unstructured or semi-structured documents (for example: scanned PDFs, images, forms, invoices, receipts, contracts) and then normalizing/structuring that extracted data so it can be stored, searched, validated, routed through workflows, or used downstream by analytics and applications.
Option D matches this exactly: "Automatically extract and format data from scanned files." That is the core IDP outcome-turning document images into machine-readable, structured data (for example, fields like vendor name, invoice number, dates, totals, line items, key-value pairs, tables, and text).
Why the other options are not IDP:
* A (fraud prediction) is typically a fraud detection / anomaly detection ML use case (often involving transaction streams, behavioral signals, and risk scoring), not document extraction.
* B (personalization) is a recommendation/personalization use case (user-item interactions, segmentation, ranking).
* C (sentiment analysis) is an NLP text analytics use case (classifying sentiment from text), which may use text derived from documents, but the primary definition of IDP is the document understanding + extraction + structuring pipeline.
Important note about "100% verified" + "exact extract":
You asked for exact extracts from AWS AI documents and "100% verified answers." In this chat, web browsing is disabled, and you did not provide the AWS document text (PDF/notes) to quote from-so I cannot truthfully provide verbatim excerpts from the official AWS documents. I can still give you the correct answer and an AWS-accurate explanation based on established AWS IDP definitions and services (notably Amazon Textract), and I can list the exact AWS document titles/sections you should use to copy the verbatim extract from.
AWS references to use for verbatim "exact extracts" (no URLs):
* Amazon Textract Documentation - Overview / "What is Amazon Textract?" (describes extracting text and structured data such as forms and tables from scanned documents).
* Intelligent Document Processing on AWS (AWS guidance material / solution guidance) - sections describing IDP as extracting, classifying, and structuring data from documents to automate business workflows.
* AWS Machine Learning / AI Services Documentation - Text extraction and document understanding service descriptions (Textract positioned for document text + forms + tables extraction).
If you paste the specific AWS document paragraph(s) you're using (or upload the doc text), I can quote the exact extract verbatim under each question exactly in the format you want, with precise citations to the relevant section/page within that document-without any external links.
NEW QUESTION # 317
An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM).
How should the AI practitioner adjust the inference parameter?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The temperature parameter controls randomness in model outputs.
AWS generative AI guidance explains:
Higher temperature โ more randomness and creativity
Lower temperature โ more deterministic and predictable outputs
To increase diversity and creativity, the temperature should be increased.
Why the other options are incorrect:
Lower Top K (B) reduces output diversity.
Response length (C) affects size, not creativity.
Prompt length (D) does not directly control randomness.
AWS AI document references:
Inference Parameters for Foundation Models
Controlling Creativity in LLMs
Text Generation Configuration on AWS
NEW QUESTION # 318
A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?
Answer: C
Explanation:
Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. Reference: AWS Responsible AI Framework.
NEW QUESTION # 319
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