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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Duration: | 90 minutes |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Exam Format: | Multiple response, Multiple choice, Ordering |
| Exam Price: | 100 USD |
| Available Languages: | Japanese, Traditional Chinese, Simplified Chinese, Korean, English |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700 (scaled score 100–1000) |
| 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 |
多くの人は結果が大丈夫で過程だけ重要ですって言いますが。AmazonのAIF-C01試験にとってはそうではない。AmazonのAIF-C01試験に合格するのはIT業界で働いているあなたに利益をもらわせることができます。もしあなたが試験に合格する決心があったら、我々のAmazonのAIF-C01ソフトを利用するのはあなたの試験に成功する有効な保障です。我々のAmazonのAIF-C01ソフトのデモをダウンロードしてみて我々Japancertのあなたに合格させる自信を感じられます。
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質問 # 190
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?
正解：C
解説：
The ecommerce company's chatbot, powered by AI, automates customer order submissions and is accessible
24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot's behavior, such as bypassing safeguards or accessing unauthorized information.
This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model's behavior, potentially leading to unauthorized actions or harmful outputs.
Implementing guardrails and input validation can mitigate this risk."
(Source: AWS Bedrock User Guide, Security Best Practices)
Detailed Explanation:
* Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot's operation.
* Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
* Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
* Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
References:
AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide
/security.html)
AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)
質問 # 191
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions. The company wants to evaluate the AI solution's return on investment over time.
正解：C
解説：
The correct answer is C, as conversion rate (how many users upgrade to premium) and retention rate (how many stay subscribed) are primary indicators of a generative AI system's business impact and ROI. According to AWS documentation, evaluating AI performance should go beyond technical accuracy to include business metrics that show real-world value. For a premium service, higher conversion implies successful personalization that attracts free users, while strong retention reflects sustained engagement and user satisfaction. Metrics like ARPU or query reduction are secondary indicators. AWS emphasizes outcome-based measurement to ensure AI initiatives contribute measurable ROI tied to customer adoption, loyalty, and profitability. Therefore, tracking conversion and retention gives the clearest long-term measure of financial success from generative AI.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - ML Business Metrics
AWS AI Adoption Framework - Value Realization and ROI Tracking
質問 # 192
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters
正解：
解説：
Explanation:
Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct:Content filters Avoid subjects related to illegal investment advice or legal advice:Denied topics Detect and block specific offensive terms:Word filters Detect and filter out information in the model's responses that is not grounded in the provided source information:Contextual grounding check The company is using a generative AI model on Amazon Bedrock and needs to mitigate undesirable and potentially harmful content in the model's responses. Amazon Bedrock provides several guardrail mechanisms, including content filters, denied topics, word filters, and contextual grounding checks, to ensure safe and accurate outputs. Each mitigation action in the hotspot aligns with a specific Bedrock filter policy, and each policy must be used exactly once.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
*"Amazon Bedrock guardrails provide mechanisms to control model outputs, including:
* Content filters: Block harmful content such as hate speech, violence, or misconduct.
* Denied topics: Prevent the model from generating responses on specific subjects, such as illegal activities or advice.
* Word filters: Detect and block specific offensive or inappropriate terms.
* Contextual grounding check: Ensure responses are grounded in the provided source information, filtering out ungrounded or hallucinated content."*(Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct: Content filtersContent filters in Amazon Bedrock are designed to detect and block harmful content, such as hate speech, insults, violence, or misconduct, ensuring the model's outputs are safe and appropriate. This matches the first mitigation action.
* Avoid subjects related to illegal investment advice or legal advice: Denied topicsDenied topics allow users to specify subjects the model should avoid, such as illegal investment advice or legal advice, which could have regulatory implications. This policy aligns with the second mitigation action.
* Detect and block specific offensive terms: Word filtersWord filters enable the detection and blocking of specific offensive or inappropriate terms defined by the user, making them ideal for this mitigation action focused on specific terms.
* Detect and filter out information in the model's responses that is not grounded in the provided source information: Contextual grounding checkThe contextual grounding check ensures that the model's responses are based on the provided source information, filtering out ungrounded or hallucinated content. This matches the fourth mitigation action.
Hotspot Selection Analysis:
The hotspot lists four mitigation actions, each with the same dropdown options: "Select...," "Content filters,"
"Contextual grounding check," "Denied topics," and "Word filters." The correct selections are:
* First action: Content filters
* Second action: Denied topics
* Third action: Word filters
* Fourth action: Contextual grounding check
Each filter policy is used exactly once, as required, and aligns with Amazon Bedrock's guardrail capabilities.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Configuring Guardrails (https://aws.amazon.com/bedrock/)
質問 # 193
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.
正解：
解説：
Explanation:
Step 1: Store meeting audio recordings in an Amazon S3 bucket.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Step 3: Summarize meeting text files by using Amazon Bedrock.
The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.
Exact Extract from AWS AI Documents:
From the Amazon Transcribe Developer Guide:
"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket." From the AWS Bedrock User Guide:
"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization) Detailed Explanation:
* Step 1: Store meeting audio recordings in an Amazon S3 bucket.Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.
* Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.
* Step 3: Summarize meeting text files by using Amazon Bedrock.Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application's requirements.
Unused Options Analysis:
* Convert meeting audio recordings to meeting text files by using Amazon Polly.Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.
* Summarize meeting text files by using Amazon Lex.Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.
Hotspot Selection Analysis:
The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps-storing in S3, converting with Transcribe, and summarizing with Bedrock-form a complete and logical workflow for the application.
References:
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)
質問 # 194
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML
正解：
解説：
Explanation:
Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.
(Reference: AWS Certified AI Practitioner Official Study Guide)
Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.
(Reference: AWS Machine Learning Overview)
Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.
(Reference: AWS Deep Learning on AWS)
質問 # 195
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AIF-C01模擬対策: https://www.japancert.com/AIF-C01.html
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