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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Exam Price: | $100 USD |
| Available Languages: | Simplified Chinese, Japanese, Korean, Portuguese (Brazil), Spanish, English |
| Exam Format: | Multiple response, Multiple choice |
| Passing Score: | 700/1000 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 65 |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Training and Certification Registration AWS Certification Portal |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
The online version of AIF-C01 quiz torrent is based on web browser usage design and can be used by any browser device. The first time you use AIF-C01 test preps on the Internet, you can use it offline next time. AIF-C01 learn torrent does not need to be used in a Wi-Fi environment, and it will not consume your traffic costs. You can practice with AIF-C01 Quiz torrent at anytime, anywhere. On the other hand, the online version has a timed and simulated exam function.
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NEW QUESTION # 330
A company is developing its first generative AI application and wants to put a responsible AI policy in place before going to production. The company is concerned with explainability and transparency with model selections for the application.
Which techniques or tools address these issues? (Select TWO.)
Answer: C,D
Explanation:
The verified answers are A. Model evaluation and C. AI model service cards. The question is specifically about explainability and transparency with model selections before a generative AI application goes to production. AWS AI Practitioner guidance identifies model selection as part of the foundation model lifecycle and includes evaluation as a required activity in that lifecycle. AWS also lists model-selection factors such as model type, performance requirements, capabilities, constraints, compliance, modality, latency, model complexity, customization, and input/output length. This means model evaluation directly supports responsible selection because it provides measurable evidence that a model meets the application's business, technical, and governance requirements.
AI model service cards are also correct because AWS describes AI Service Cards as responsible AI documentation that improves transparency. AWS states that AI Service Cards provide information about intended use cases, limitations, responsible AI design choices, and performance optimization best practices for AWS AI services and models. AWS also links these cards to responsible AI areas such as fairness, bias, explainability, robustness, governance, transparency, privacy, and security. That directly addresses the company's concern about transparency when choosing models.
Guardrails is not the best answer here. Guardrails are important for safety controls, denied topics, content filtering, and responsible runtime behavior, but the question focuses on explainability and transparency in model selection, not output filtering. Data encryption is incorrect because encryption protects data confidentiality at rest or in transit; it does not explain model behavior or document model limitations. Automated reasoning is also not the best match. It can support formal validation of specific policy-based logic, but it is not the primary AWS tool or technique for transparent model selection. Therefore, model evaluation and AI model service cards are the two techniques/tools that best address the stated requirement.
NEW QUESTION # 331
A company wants more customized responses to its generative AI models ' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
NEW QUESTION # 332
A company is using Amazon Bedrock Agents to build an application to automate business workflows.
Answer: A
NEW QUESTION # 333
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)
Answer: B,E
Explanation:
To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.
* Amazon Bedrock:
* Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.
* The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces like chatbots, not for sentiment analysis.
* C. Amazon Polly: Converts text to speech; it doesn't analyze sentiment.
* E. Amazon Rekognition: Analyzes images and videos, not text.
NEW QUESTION # 334
A company uses Amazon Bedrock to implement a generative AI solution. The AI solution provides customers with personalized product recommendations.
The company wants to evaluate the impact of the AI solution on sales revenue.
Which metric will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Conversion rate measures the percentage of users who take a desired business action, such as completing a purchase after receiving recommendations.
In the context of:
* Personalized product recommendations
* Measuring direct impact on sales revenue
Conversion rate is the most relevant business metric, as it directly correlates AI-driven recommendations with revenue-generating outcomes.
Why the other options are incorrect:
* Cross-domain performance (A) evaluates generalization across datasets, not revenue.
* Solution efficiency (B) focuses on performance and cost, not sales impact.
* User satisfaction (C) measures experience quality, not financial outcomes.
AWS AI document references:
* AWS Generative AI Use Case Evaluation Guidance
* Measuring Business Impact of AI Solutions
* AI Metrics and KPIs on AWS
NEW QUESTION # 335
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