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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Exam Price: | $100 USD |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple response, Multiple choice |
| Available Languages: | Spanish, English, Simplified Chinese, Portuguese (Brazil), Japanese, Korean |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| 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 # 100
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: B
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 # 101
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 102
A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?
Answer: D
Explanation:
Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.
D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.
A (reducing data size) could worsen bias by removing potentially useful diverse data.
B (consistency with historical results) might reinforce existing biases.
C (separate models) is not scalable and can introduce other fairness issues.
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
NEW QUESTION # 103
A media company wants to analyze viewer behavior and demographics to recommend personalized content.
The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?
Answer: B
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:
A). Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.
B). Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions.
While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.
C). Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.
D). Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, "Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation." (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model- monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.
References:
AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg
/model-monitor.html)
AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)
NEW QUESTION # 104
A company wants to implement a generative AI assistant to provide consistent responses to various phrasings of user questions.
Which advantages can generative AI provide in this use case?
Answer: B
Explanation:
The correct answer is B - Adaptability and responsiveness, which are core strengths of generative AI models such as the foundation models available in Amazon Bedrock. According to AWS documentation, generative AI systems excel at understanding natural language variations, meaning they can interpret different phrasings, synonyms, sentence structures, and conversational styles while still generating contextually consistent answers. This capability comes from pretraining on diverse natural language corpora, allowing models to generalize across multiple linguistic patterns. AWS highlights that generative AI models are designed to handle "flexible, dynamic, and conversational inputs" and provide responses grounded in understanding user intent rather than matching exact keywords. Options A and D describe infrastructure performance characteristics, not the reasoning ability required for this use case. Option C (deterministic outputs) is incorrect because LLMs are inherently probabilistic and not fixed unless using advanced constraints.
Therefore, generative AI's adaptability to varied user phrasing makes it ideal for assistants requiring consistent, intent-based responses.
Referenced AWS Documentation:
* Amazon Bedrock Developer Guide - Foundation Model Capabilities
* AWS Generative AI Best Practices - Natural Language Understanding
NEW QUESTION # 105
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