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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | USD 100 |
| Exam Duration: | 90 minutes |
| Available Languages: | Korean, English, Japanese, Simplified Chinese, Portuguese (Brazil) |
| Exam Format: | Multiple response, Multiple choice |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700 / 1000 |
| Real Exam Qty: | 80 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning Engineer - Associate AWS Certified Data Engineer - Associate |
| 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 # 261
An online learning company with large volumes of education materials wants to use enterprise search.
Answer: A
Explanation:
The correct answer is C - Amazon Kendra, AWS's enterprise search service designed for organizations with large, diverse document repositories. Kendra uses machine learning and natural language understanding (NLU) to provide semantic search, meaning it retrieves results based on meaning rather than keyword matching. According to AWS documentation, Kendra is ideal for educational, enterprise, and knowledge- management scenarios where users need fast, accurate retrieval across PDFs, HTML, Office documents, FAQs, and multimedia transcripts. Kendra connectors can index content from S3, SharePoint, LMS platforms, and internal databases, making it perfect for large volumes of training materials. Amazon Comprehend (A) is for NLP tasks like entity extraction, not enterprise search. Amazon Textract (B) extracts text from PDFs and scanned materials but does not provide search capabilities. Amazon Personalize (D) is for personalized recommendations, not document retrieval. Kendra is purpose-built for enterprise search and aligns directly with the company's needs.
Referenced AWS Documentation:
* Amazon Kendra Developer Guide - Enterprise Search
* AWS ML Specialty Guide - Intelligent Search Systems
NEW QUESTION # 262
A company wants to set up private access to Amazon Bedrock APIs from the company's AWS account. The company also wants to protect its data from internet exposure.
Answer: C
Explanation:
AWS PrivateLink enables private connectivity between your VPC and supported AWS services (like Amazon Bedrock) without sending traffic over the public internet.
CloudFront (A) is for CDN and content delivery, not private service connections.
AWS Glue (B) is for ETL/data catalog, not networking.
Lake Formation (C) provides governance for data lakes, not API network isolation.
Reference:
AWS Documentation - Access Amazon Bedrock with PrivateLink
NEW QUESTION # 263
A company wants to customize a foundation model (FM). The company wants to understand the customization methods and data types that are available.
Select the correct customization method from the following list for each description. Select each customization method one time. (Select THREE.) Customization methods:
* Continued pre-training
* Distillation
* Fine-tuning
Answer:
Explanation:
Explanation:
Provide labeled data to customize a model to improve performance on specific tasks.
answer: Fine-tuning
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI guidance defines fine-tuning as the process of adapting a pre-trained foundation model using labeled, task-specific data. Fine-tuning adjusts the model's parameters so it performs better on a particular task, such as classification, summarization, or domain-specific reasoning.
Fine-tuning is commonly used when:
* High-quality labeled data is available
* The goal is to improve accuracy on a specific task
* The base FM already has strong general capabilities
AWS AI Study Guide References:
* AWS foundation model customization methods
* AWS fine-tuning concepts for generative AI
Provide unlabeled data to customize a foundation model for a specific domain.
answer: Continued pre-training
Comprehensive and Detailed Explanation (AWS AI documents):
AWS documentation describes continued pre-training as extending the training of a foundation model using large volumes of unlabeled, domain-specific data. This method helps the model better understand domain vocabulary, structure, and context without requiring labeled datasets.
Continued pre-training is useful when:
* Large amounts of unlabeled domain data are available
* The goal is to improve domain understanding rather than a single task
* Labeling data would be expensive or impractical
AWS AI Study Guide References:
* AWS generative AI training lifecycle
* AWS guidance on domain adaptation using unlabeled data
Transfer knowledge from a larger and more intelligent model to a smaller model.
answer: Distillation
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI materials define distillation as a technique where a smaller model (student) learns to replicate the behavior of a larger, more capable model (teacher). The goal is to retain most of the performance while reducing model size, cost, and inference latency.
Distillation is commonly used to:
* Reduce operational costs
* Improve inference speed
* Deploy models to resource-constrained environments
AWS AI Study Guide References:
* AWS model optimization techniques
* AWS knowledge distillation concepts
NEW QUESTION # 264
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: B
Explanation:
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.
NEW QUESTION # 265
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.
Which technique will improve the model's accuracy?
Answer: A
Explanation:
A: Transfer learning: This involves pre-training a model on a large dataset and fine-tuning it for a specific task. While effective for general model improvement, it does not specifically address the challenge of incorporating rapidly changing inventory data into the model's responses.
B: Federated learning: This technique trains models across decentralized devices while keeping data localized, primarily for privacy purposes. It is not designed to handle rapidly changing data or improve NLG model accuracy in this context.
C: Retrieval Augmented Generation (RAG): RAG combines a language model with a retrieval mechanism that fetches relevant, up-to-date information (e.g., inventory data) from an external source during inference. This is ideal for scenarios with dynamic data, as it ensures the model's responses are grounded in the latest information, improving accuracy.
D: One-shot prompting: This involves providing a single example to guide the model's output. While useful for specific tasks, it does not scale well for rapidly changing data or ensure consistent accuracy with dynamic inventory updates.
Exact Extract Reference: According to AWS documentation on generative AI techniques, "Retrieval Augmented Generation (RAG) enhances large language models by retrieving relevant documents or data at inference time, enabling the model to generate accurate and contextually relevant responses, especially for dynamic or frequently updated datasets." (Source: AWS Generative AI Glossary, https://aws.amazon.com/what-is/retrieval-augmented-generation/). This directly addresses the need for accuracy with rapidly changing inventory data.
RAG is the most suitable technique for this scenario, as it allows the model to access and incorporate the latest inventory data, making C the correct answer.
Explanation:
The requirement is to improve the accuracy of a natural language generation (NLG) model that relies on rapidly changing inventory data. Let's evaluate the options:
Reference:
AWS Generative AI Glossary: Retrieval Augmented Generation (https://aws.amazon.com/what-is/retrieval-augmented-generation/) AWS Bedrock Documentation (contextual use of RAG in LLMs) AWS AI Practitioner Study Guide (focus on generative AI techniques for dynamic data)
NEW QUESTION # 266
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