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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 |
| Passing Score: | 700 (scaled score 100–1000) |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| Exam Price: | 100 USD |
| Available Languages: | Traditional Chinese, English, Japanese, Korean, Simplified Chinese |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Multiple response, Multiple choice, Ordering |
| 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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67. Frage
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?
Antwort: A
Begründung:
The verified answer is A. Review the training data to check for biases. Include data from all demographics in the training data. The application is being used for loan approval decisions, so fairness is critical. AWS Machine Learning Lens guidance states that teams should analyze whether training data adequately represents the diversity of the user population and check for existing biases in labels or features that could be perpetuated by the model. AWS also recommends evaluating and preparing representative training data, analyzing training data for potential biases, verifying that the data accurately represents the population on which the model will be deployed, and addressing representation gaps.
This directly matches option A. For loan decisions, biased or unrepresentative training data can cause unfair outcomes for demographic groups. Reviewing the training data and including representative data from all demographics helps reduce the risk that the model learns patterns that disadvantage underrepresented groups.
Option B is incorrect because a deep learning model with many hidden layers does not automatically make a system fair. In fact, more complex models can be harder to interpret and may still learn biased patterns from biased data.
Option C is incorrect because secrecy conflicts with responsible AI principles. Financial loan decisions often require transparency, explainability, governance, and auditability. Hiding the decision process does not make outputs fair.
Option D is incorrect because monitoring only a static test dataset is insufficient. A static dataset may not represent changing real-world populations, drift, or emerging bias. AWS guidance recommends tracking fairness metrics over time and detecting emerging bias in deployment.
Therefore, the correct solution is to review and balance representative training data across demographics.
68. Frage
A multinational company is experiencing rapid growth. The company needs to scale AI initiatives and help employees efficiently find, access, and properly use company data in compliance with established policies and standards.
Which solution will meet these requirements?
Antwort: C
Begründung:
The verified answer is C. Add unified governance, discovery, and collaboration capabilities to Amazon SageMaker AI. The question is not asking only for storage, monitoring, or a basic repository. It asks for a scalable way for employees to find, access, and properly use company data while remaining compliant with policies and standards. AWS documentation for the next generation of Amazon SageMaker states that it simplifies discovery, governance, and collaboration for data and AI. Amazon SageMaker Catalog helps users securely discover and access approved data and assets through semantic search and governance workflows.
This directly matches the company's requirement. In a rapidly growing multinational organization, data becomes harder to locate, understand, govern, and reuse. Unified governance and catalog capabilities allow teams to publish, discover, request access to, and collaborate on approved assets. AWS also describes Amazon SageMaker Catalog as a way to discover, govern, and collaborate on structured data, unstructured data, AI models, BI dashboards, and applications from a single catalog.
Option A is incorrect because a repository without integration does not solve governance, collaboration, or policy-based access. Option B is incomplete because storage alone does not help employees find or properly use data. Option D is also incomplete because monitoring can detect issues, but it does not provide data discovery, governance workflows, or collaboration features.
The correct solution must combine discovery, access governance, metadata, collaboration, and policy-aligned usage. Therefore, option C is the correct answer.
69. Frage
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.
Which technique will improve the model's accuracy?
Antwort: D
Begründung:
The requirement is to improve the accuracy of a natural language generation (NLG) model that relies on rapidly changing inventory data. Let's evaluate the options:
A). Transfer learning: This involves pre-training a model on a large dataset and fine-tuning it for a specific task. While effective for general model improvement, it does not specifically address the challenge of incorporating rapidly changing inventory data into the model's responses.
B). Federated learning: This technique trains models across decentralized devices while keeping data localized, primarily for privacy purposes. It is not designed to handle rapidly changing data or improve NLG model accuracy in this context.
C). Retrieval Augmented Generation (RAG): RAG combines a language model with a retrieval mechanism that fetches relevant, up-to-date information (e.g., inventory data) from an external source during inference.
This is ideal for scenarios with dynamic data, as it ensures the model's responses are grounded in the latest information, improving accuracy.
D). One-shot prompting: This involves providing a single example to guide the model's output. While useful for specific tasks, it does not scale well for rapidly changing data or ensure consistent accuracy with dynamic inventory updates.
Exact Extract Reference: According to AWS documentation on generative AI techniques, "Retrieval Augmented Generation (RAG) enhances large language models by retrieving relevant documents or data at inference time, enabling the model to generate accurate and contextually relevant responses, especially for dynamic or frequently updated datasets." (Source: AWS Generative AI Glossary, https://aws.amazon.com
/what-is/retrieval-augmented-generation/). This directly addresses the need for accuracy with rapidly changing inventory data.
RAG is the most suitable technique for this scenario, as it allows the model to access and incorporate the latest inventory data, making C the correct answer.
References:
AWS Generative AI Glossary: Retrieval Augmented Generation (https://aws.amazon.com/what-is/retrieval- augmented-generation/) AWS Bedrock Documentation (contextual use of RAG in LLMs) AWS AI Practitioner Study Guide (focus on generative AI techniques for dynamic data)
70. Frage
A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.
Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)
Antwort: A,C
Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
In a RAG (Retrieval Augmented Generation) architecture, there are steps that can be optimized using offline batch processing, particularly for operations that do not require real-time updates:
A . Generation of content embeddings:
When new content is published, it can be processed in batches to generate embeddings (vector representations) offline. These embeddings are then used at query time for similarity search. As new documents come in daily, batch processing is ideal for generating embeddings for all new content together.
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern)
"Content/document embeddings are typically generated offline, as this operation can be computationally expensive and does not need to happen in real-time." (Reference: AWS GenAI RAG Blog, Amazon Bedrock RAG Pattern) C . Creation of the search index:
After generating the content embeddings, these are indexed in a vector database or search service. This indexing is also typically performed in batch as part of the offline pipeline.
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper)
"Building or updating the vector index is often performed as a batch operation, reflecting the latest state of the content repository." (Reference: AWS RAG Pattern Whitepaper) B, D, and E are real-time steps. Embeddings for user queries (B), retrieval of relevant content (D), and response generation (E) must be processed in real-time to provide an interactive experience.
Reference:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
71. Frage
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
Antwort: B
Begründung:
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.
* Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.
* Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.
* Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine- tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.
* Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.
AWS AI Practitioner References:
* Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.
72. Frage
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AIF-C01 Exam Fragen: https://www.zertpruefung.de/AIF-C01_exam.html
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