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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Available Languages: | Simplified Chinese, Traditional Chinese, Japanese, English, Korean |
| Exam Format: | Multiple response, Ordering, Multiple choice |
| Exam Price: | 100 USD |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| Passing Score: | 700 (scaled score 100–1000) |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Path AWS Certified AI Practitioner Official Training |
| Exam Registration: | AWS Certification Registration Pearson VUE Scheduling |
| 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 |
>> Amazon AIF-C01 Fragenkatalog <<
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99. Frage
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
Antwort: A
Begründung:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
* Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
* Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
* Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
* Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner References:
* Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences
100. Frage
An AI practitioner has prepared a dataset for training models in Amazon SageMaker AI. The AI practitioner wants to share the dataset within the company so that future employees can discover and reuse the dataset.
Which solution will meet these requirements?
Antwort: B
Begründung:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends Amazon SageMaker Feature Store as a centralized, managed repository for storing, discovering, and reusing features and datasets across teams and projects. Feature Store enables:
Feature discovery through searchable metadata
Reusability of curated datasets and features by future practitioners
Consistency between training and inference datasets
Governance and lineage, supporting responsible AI practices
Why the other options are incorrect:
A is intended for retrieval-augmented generation, not dataset sharing.
B does not provide discoverability, governance, or long-term reuse.
D is designed for sharing data with external subscribers, not internal ML teams.
AWS AI Study Guide Reference:
Amazon SageMaker Feature Store overview
AWS best practices for feature management and reuse
101. Frage
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
Antwort:
Begründung:
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/)
102. Frage
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?
Antwort: C
Begründung:
Amazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic language using pre-trained models-no need for labeled data or custom training.
B is correct: Comprehend's toxicity detection API is designed for this use case, works out-of-the-box, and requires no data labeling or model training.
A (Rekognition) is for image and video content moderation.
C would require labeled data for training.
D (Polly) is for text-to-speech, not content moderation.
"Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data." (Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
"Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data." (Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)
103. Frage
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
Antwort: A
104. Frage
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