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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Machine Learning Engineer - Associate AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate |
| Passing Score: | 700 / 1000 |
| Available Languages: | Korean, Japanese, English, Simplified Chinese, Portuguese (Brazil) |
| Exam Price: | USD 100 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple response, Multiple choice |
| Real Exam Qty: | 80 |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 417
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?
Answer: D
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
"SageMaker Model Cards provide a single source of truth for model information including intended use, training details, evaluation metrics, and ethical considerations to support governance and collaboration."
NEW QUESTION # 418
Which AWS service helps select foundation models (FMs) for generative AI use cases?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock provides access to multiple foundation models from different providers and enables customers to evaluate, compare, and select the most appropriate model for their generative AI use cases.
Amazon Bedrock:
* Offers a choice of foundation models
* Supports model evaluation and customization
* Abstracts infrastructure management
Why the other options are incorrect:
* Amazon Personalize (A) is a recommendation service.
* Amazon Q Developer (C) is a coding assistant.
* Amazon Rekognition (D) is an image and video analysis service.
AWS AI document references:
* Amazon Bedrock Overview
* Choosing Foundation Models on AWS
* Generative AI Model Selection Guidance
NEW QUESTION # 419
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?
Answer: B
Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.
NEW QUESTION # 420
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
Answer: D
Explanation:
The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
"A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential." Explanation of other options:
B . The number of hidden layers doesn't inherently improve fairness or responsibility.
C . Keeping decisions opaque violates explainability principles in responsible AI.
D . A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Bias Detection and Explainability AWS Responsible AI Guidelines AWS ML Specialty Study Guide - Fairness and Governance
NEW QUESTION # 421
A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.
Which solution meets these requirements?
Answer: C
Explanation:
Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch predictions, allowing companies to deploy ML models without handling any servers or infrastructure management.
D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure overhead.
A and B require infrastructure management.
C (CloudFront/S3) is not for model deployment, but for static content delivery.
"Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure." (Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)
"Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure." (Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)
NEW QUESTION # 422
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