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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Available Languages: | Japanese, Simplified Chinese, English, Korean, Traditional Chinese |
| Exam Format: | Ordering, Multiple response, Multiple choice |
| Exam Duration: | 90 minutes |
| Exam Price: | 100 USD |
| Passing Score: | 700 (scaled score 100–1000) |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| 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 |
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NEW QUESTION # 167
Which option is a use case for generative AI models?
Answer: C
Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner Reference:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
NEW QUESTION # 168
A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.
The company collected a labeled dataset and wants to scale the solution to all product categories.
Which solution meets these requirements?
Answer: A
Explanation:
When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.
D is correct:
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI) A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.
C (continued pre-training) uses unlabeled data, not labeled.
NEW QUESTION # 169
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
NEW QUESTION # 170
Which term is an example of output vulnerability?
Answer: B
NEW QUESTION # 171
A company wants to use Amazon Q Business for its dat
a. The company needs to ensure the security and privacy of the data. Which combination of steps will meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
The correct answers are A and E because both directly align with AWS best practices for securing generative AI services and data privacy in enterprise applications.
From the AWS Amazon Q Business documentation:
"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest. You can use customer-managed KMS keys to meet compliance requirements." And:
"You must configure IAM access controls to manage which users and applications can access Amazon Q Business indexes, ensuring that only authorized users can retrieve information." Explanation of other options:
B . Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business unless explicitly sharing data across organizations. It's not a requirement for securing access.
C . Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q authentication or security.
D . Allowing public access would violate security and privacy principles and directly contradict the stated requirement.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Q Business Developer Guide - Security and Identity Management
AWS KMS Documentation - Integration with Bedrock and Amazon Q
AWS Certified Machine Learning Specialty Guide - Responsible AI and Governance Section
NEW QUESTION # 172
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