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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Available Languages: | Japanese, Korean, English, Simplified Chinese, Portuguese (Brazil), Spanish |
| Passing Score: | 700/1000 |
| Exam Format: | Multiple response, Multiple choice |
| Exam Price: | $100 USD |
| Real Exam Qty: | 65 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Recommended Training: | AWS Certified AI Practitioner Official Exam Guide AWS Skill Builder - AI Practitioner Learning Plan |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| 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/ |
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NEW QUESTION # 119
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.
Which option represents the model's problem?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
* Interpretability refers to understanding how a model arrives at its predictions.
* Nondeterminism refers to variation in outputs across runs due to probabilistic sampling.
* Accuracy is a general performance metric, not the specific phenomenon described.
AWS AI Study Guide References:
* AWS generative AI challenges and limitations
* AWS guidance on hallucinations in foundation models
NEW QUESTION # 120
A company wants to ensure that its Retrieval Augmented Generation (RAG) system retrieves all relevant documents for user queries without missing important information.
Which metric should the company track?
Answer: D
Explanation:
The verified answer is B. Recall. In a Retrieval Augmented Generation system, the retrieval component must find the documents or passages that contain the information needed to answer a user query. The question specifically says the company wants to retrieve all relevant documents and avoid missing important information. That wording points directly to recall. Recall measures how many of the relevant items were successfully retrieved out of all relevant items that should have been retrieved. In practical RAG evaluation, high recall means the retriever is less likely to omit important source documents from the context sent to the language model.
Faithfulness is incorrect because it evaluates whether the generated answer is grounded in, and consistent with, the retrieved context. It is a generation-quality metric, not the primary retrieval-coverage metric. A response can be faithful to retrieved context even if the retriever failed to include some important documents. Mean reciprocal rank is incorrect because it measures how highly the first relevant result appears in the ranked list. It is useful for ranking quality, but it does not directly confirm that all relevant documents were retrieved. Precision is incorrect because precision measures how many retrieved documents are actually relevant. High precision reduces irrelevant results, but it can still miss relevant documents if the retriever returns only a narrow subset.
AWS documentation separates retrieval-focused evaluation metrics from response-generation metrics. For knowledge base retrieval-only evaluations, AWS lists built-in retrieval metrics such as context relevance and context coverage, while response generation evaluations include metrics such as faithfulness, correctness, completeness, and helpfulness. This distinction supports the principle that retrieval coverage and not missing relevant information is a retrieval-side concern. In standard information retrieval terminology, the metric that best captures "do not miss relevant documents" is recall.
NEW QUESTION # 121
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
Answer: C
NEW QUESTION # 122
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.
Answer:
Explanation:
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)
NEW QUESTION # 123
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
Answer: D
Explanation:
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.
NEW QUESTION # 124
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