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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Format: | Multiple response, Multiple choice |
| Available Languages: | Simplified Chinese, Japanese, Spanish, English, Korean, Portuguese (Brazil) |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Exam Price: | $100 USD |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700/1000 |
| Real Exam Qty: | 65 |
| Exam Duration: | 90 minutes |
| Recommended Training: | AWS Certified AI Practitioner Official Exam Guide AWS Skill Builder - AI Practitioner Learning Plan |
| 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/ |
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NEW QUESTION # 445
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 446
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Which human-centered design principle does this scenario present?
Answer: D
Explanation:
Explainability refers to the ability of an AI system to make its decision-making process clear and understandable to humans.
A is correct:
"Explainability is crucial for human-centered AI, especially in healthcare, to ensure that doctors and patients understand the rationale behind AI-driven recommendations." (Reference: AWS Responsible AI) B relates to protecting data, not explanations.
C is about treating groups equally.
D is about managing data lifecycle, not providing rationales.
NEW QUESTION # 447
A company is exploring Amazon Nova models in Amazon Bedrock. The company needs a multimodal model that supports multiple languages.
Answer: D
Explanation:
* Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.
* Nova Lite is optimized for lightweight, faster inference at lower cost.
* Nova Canvas is a creative tool for visual design.
* Nova Reel is optimized for video-related use cases.
# Reference:
AWS Documentation - Amazon Nova Models
NEW QUESTION # 448
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 # 449
A company wants to use an AI model to generate labels for online news articles that the company publishes. The company selects a foundation model (FM) instead of a conventional ML model for this task.
What is one advantage of using an FM instead of a conventional ML model to meet this requirement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
According to AWS Generative AI and Foundation Model guidance, foundation models are pre-trained on very large and diverse datasets, enabling them to perform a wide range of tasks such as classification, summarization, labeling, and content generation without task-specific training.
For labeling online news articles, a foundation model can be used out of the box or with minimal prompt engineering, whereas a conventional ML model would typically require:
Task-specific labeled training data
Model training and validation cycles
Ongoing retraining as content evolves
Why the other options are incorrect:
B . Smaller and faster - Foundation models are generally larger, not smaller, than conventional ML models.
C . More transparent - FMs are often less transparent due to their size and complexity.
D . Not biased - Foundation models can still inherit biases from training data; bias mitigation is required.
AWS AI Study Guide Reference:
AWS Generative AI fundamentals
AWS Foundation Model characteristics and benefits
NEW QUESTION # 450
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