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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Available Languages: | Korean, Traditional Chinese, Japanese, Simplified Chinese, English |
| Passing Score: | 700 (scaled score 100โ1000) |
| Exam Format: | Multiple choice, Multiple response, Ordering |
| Related Certifications: | AWS Certified Machine Learning โ Specialty AWS Certified Cloud Practitioner |
| Exam Price: | 100 USD |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| 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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NEW QUESTION # 51
A company is using Amazon Bedrock Agents to build an application to automate business workflows.
Answer: D
Explanation:
The correct answer is D. Amazon Bedrock Agents are used to orchestrate and execute complex workflows by connecting foundation models with APIs, databases, and tools. According to AWS documentation, agents interpret user inputs, plan the necessary steps, call external APIs or systems, and return structured results. This allows the model to go beyond text generation into full automation workflows-such as booking tasks, querying internal systems, or summarizing reports. Option A describes multi-modal models, B refers to prompt tuning, and C misstates control delegation; agents act autonomously based on model reasoning. Thus, Bedrock Agents function as intelligent orchestrators, handling multi-step task execution through integrated tool use.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Agents Overview
AWS Generative AI Best Practices - Workflow Orchestration
NEW QUESTION # 52
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.)
Answer: B,C
Explanation:
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)
* 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) 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.
References:
Retrieval Augmented Generation (RAG) on AWS
Amazon Bedrock RAG Documentation
NEW QUESTION # 53
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?
Answer: C
Explanation:
Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.
NEW QUESTION # 54
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce.
Which AWS service or feature meets these requirements?
Answer: D
Explanation:
* Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
* Data Wrangler is for data preparation and transformation.
* Transcribe is for speech-to-text.
* Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation - SageMaker Ground Truth Plus
NEW QUESTION # 55
A company wants to assess internet quality in remote areas of the world. The company needs to collect internet speed data and store the data in Amazon RDS. The company will analyze internet speed variation throughout each day. The company wants to create an AI model to predict potential internet disruptions.
Which type of data should the company collect for this task?
Answer: D
NEW QUESTION # 56
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