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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | 100 USD |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Multiple response, Multiple choice, Ordering |
| Certificate Validity Period: | 3 years |
| Available Languages: | Japanese, English, Korean, Traditional Chinese, Simplified Chinese |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning โ Specialty |
| Passing Score: | 700 (scaled score 100โ1000) |
| 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 # 336
A fitness company has an application that uses LLMs to create new personalized exercise routines for users.
The company generates the routines every week for all users in the company's database.
The company wants to reduce costs for this repetitive workload. The workload processes large volumes of requests and does not require immediate responses.
Which solution will meet these requirements?
Answer: C
Explanation:
The verified answer is C. Use batch inference with Amazon Bedrock. The workload has three decisive characteristics: it is repetitive, it processes a large volume of requests, and it does not require immediate responses. AWS documentation states that with Amazon Bedrock batch inference, users can submit multiple prompts and generate responses asynchronously. It also states that batch inference helps process a large number of requests efficiently by sending a single request and generating responses in an Amazon S3 bucket.
That directly matches the weekly generation of exercise routines for all users.
AWS also explains that after the batch job is complete, the output files can be retrieved from Amazon S3, and batch inference can improve model inference performance on large datasets. This is the right architecture for offline or scheduled workloads where immediate response latency is not required.
Amazon Bedrock AgentCore is incorrect because AgentCore is for building, running, and managing agents, tools, memory, gateways, and agent workflows. The problem is not agent orchestration; it is cost-efficient high-volume offline inference.
Real-time inference with Amazon Bedrock on-demand endpoints is incorrect because real-time inference is better for interactive applications where users need immediate responses. The question explicitly says immediate responses are not required.
Real-time inference with SageMaker AI hosted endpoints is also incorrect for the same reason. Hosted endpoints are appropriate for low-latency inference requests, but this workload is scheduled and large-volume.
Therefore, Amazon Bedrock batch inference is the best solution.
NEW QUESTION # 337
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: A
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 # 338
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
Answer: D
Explanation:
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 339
An ecommerce company is developing an AI application that categorizes product images and extracts specifications. The application will use a high-quality labeled dataset to customize a foundation model (FM) to generate accurate responses.
Which ML technique will meet these requirements by using Amazon Bedrock?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The correct technique is fine-tuning, which is explicitly supported by Amazon Bedrock for customizing foundation models using high-quality labeled datasets.
Fine-tuning involves:
* Starting with a pre-trained foundation model
* Training it further using domain-specific, labeled data
* Improving accuracy for specialized tasks, such as product classification, image-based understanding, and specification extraction In this use case:
* The company has labeled data
* They want to customize model behavior
* They require high accuracy and domain adaptation
These conditions match the definition of fine-tuning, not prompt-only methods.
Why the other options are incorrect:
* A. Continued pre-training typically requires massive unlabeled datasets and is not the standard customization method exposed in Amazon Bedrock.
* B. Creating an agent orchestrates model interactions and tools but does not customize the model's learned parameters.
* D. Prompt engineering improves responses through prompt design but does not modify the underlying model weights, making it insufficient for deep domain adaptation.
AWS AI document references (for exact extracts):
* Amazon Bedrock Documentation - section on Model customization and fine-tuning
* AWS Generative AI Study Guide - comparison of prompt engineering vs fine-tuning
* Foundation Models on AWS - explanation of fine-tuning with labeled datasets
NEW QUESTION # 340
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: A
Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
* Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
* Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
* Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
* Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
* Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
NEW QUESTION # 341
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