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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Available Languages: | Japanese, English, Simplified Chinese, Traditional Chinese, Korean |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Price: | 100 USD |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Exam Format: | Ordering, Multiple choice, Multiple response |
| 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 |
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NEW QUESTION # 175
A company has a generative AI application that uses a pre-trained foundation model (FM) on Amazon Bedrock. The company wants the FM to include more context by using company information.
Which solution meets these requirements MOST cost-effectively?
Answer: D
Explanation:
Amazon Bedrock Knowledge Bases enable Retrieval Augmented Generation (RAG) by letting you connect external company data sources to your foundation model in a serverless, cost-effective manner-without retraining or fine-tuning. This allows the model to answer questions and generate content grounded in your own documents or data.
A is correct:
"Knowledge Bases for Amazon Bedrock enable generative AI applications to retrieve and include your company's information for more contextual responses, without the need for expensive retraining or custom models." (Reference: Amazon Bedrock Knowledge Bases) B is not cost-effective or guaranteed to add your company's context.
C (Agents) are for orchestrating workflows, not specifically RAG/context.
D (Custom model deployment) is costly and unnecessary for just adding context.
NEW QUESTION # 176
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.
AWS generative AI guidance explains that:
* Prompts can define tone, voice, and response structure
* Iterative refinement ensures consistent outputs
* Prompt refinement requires no model retraining
Why the other options are incorrect:
* Token limits (A) affect length, not tone.
* Batch inferencing (B) affects processing mode, not response style.
* Higher temperature (D) increases randomness, reducing consistency.
AWS AI document references:
* Prompt Engineering Best Practices
* Controlling Model Output Tone
* Using Prompts with Foundation Models on AWS
NEW QUESTION # 177
A company is using Amazon SageMaker AI to develop AI/ML solutions. The company must use only approved data for model training. The AI/ML solutions must comply with company policy and ethical guidelines.
Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon SageMaker Model Cards provide a structured way to document:
* Approved training datasets
* Intended use cases
* Ethical considerations and limitations
* Compliance with internal policies and governance standards
Model Cards are a Responsible AI governance tool, enabling transparency and accountability throughout the model lifecycle.
Why the other options are incorrect:
* Catalog (A) organizes ML assets but does not enforce ethical or policy documentation.
* Clarify (B) detects bias and explainability issues but does not govern approved data usage.
* Model Registry (C) manages model versions and approvals but does not document ethical intent.
AWS AI document references:
* Amazon SageMaker Model Cards Documentation
* AWS Responsible AI Practices
* AI Governance on AWS
NEW QUESTION # 178
A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
Answer: A
Explanation:
The correct answer is B - Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Batch Inference
AWS ML Specialty Study Guide - Scalable Inference Options
NEW QUESTION # 179
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Answer:
Explanation:
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
NEW QUESTION # 180
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