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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Available Languages: | Korean, English, Simplified Chinese, Traditional Chinese, Japanese |
| Exam Price: | 100 USD |
| Passing Score: | 700 (scaled score 100–1000) |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Ordering, Multiple choice, Multiple response |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Path AWS Certified AI Practitioner Official Training |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| 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 # 256
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
NEW QUESTION # 257
A financial company is creating an AI model for customer loan applications. The company wants to demonstrate the principles of human-centered design for explainable AI.
Which Amazon SageMaker AI feature meets these requirements?
Answer: C
Explanation:
The verified answer is B. Amazon SageMaker Clarify. The model is used for customer loan applications, where explainability, transparency, and human-centered decision support are critical. AWS documentation states that SageMaker Clarify provides tools to help explain how machine learning models make predictions. These tools help ML developers, modelers, and internal stakeholders understand model characteristics before deployment and debug predictions after deployment.
SageMaker Clarify is specifically aligned with explainable AI because it provides feature attribution methods, including SHAP-based explanations. AWS documentation explains that Clarify can generate reports that contribute to model governance documentation and can help describe how a model works, what features influence its decisions, and how explanations are generated.
Amazon SageMaker Model Registry is incorrect because it manages model versions, approval status, and deployment tracking. It supports governance workflows, but it does not explain why a model made a decision.
Amazon SageMaker Pipelines is incorrect because it automates and orchestrates ML workflows such as training, evaluation, and deployment. It does not provide the core explainability function required here.
Amazon SageMaker Feature Store is incorrect because it stores, shares, and serves ML features for training and inference. It improves feature reuse and consistency, but it does not explain model decisions.
NEW QUESTION # 258
Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all. (Select THREE.)
* Chain-of-thought prompting
* Few-shot prompting
* Role-based prompting
* Single-shot prompting
* Zero-shot prompting
Answer:
Explanation:
Explanation:
The verified selections are Few-shot prompting , Chain-of-thought prompting , and Zero-shot prompting . The first description matches few-shot prompting because AWS describes few-shot prompting as a technique that includes example outputs or demonstrations in the initial prompt so the model can understand the expected pattern before generating a response. The phrase "provide a small number of examples" is the key indicator.
A few-shot prompt gives the model limited examples of the desired task, format, or reasoning style, and the model uses those examples as context for the next output.
The second description matches chain-of-thought prompting . AWS describes chain-of-thought prompting as a technique that helps a model solve a problem by following a series of intermediate reasoning steps before reaching the final answer. The wording "break down the step-by-step process" directly points to chain-of- thought prompting. This method is commonly associated with reasoning, arithmetic, logic, planning, and multi-step problem solving because the prompt encourages the model to work through intermediate steps rather than immediately outputting a final answer.
The third description matches zero-shot prompting because AWS describes zero-shot prompting as asking the model to perform a task without providing examples in the prompt. The model relies only on the instruction and its pre-trained knowledge. The phrase "without providing examples" is the decisive clue.
Role-based prompting is not used here because none of the descriptions asks the model to act as a specific persona, job role, or domain expert, such as "Act as a financial analyst" or "You are a security engineer." Single-shot prompting is also not used because the first description says a "small number of examples," which indicates few-shot prompting, not exactly one example. Therefore, the three correct hotspot mappings are Few-shot prompting , Chain-of-thought prompting , and Zero-shot prompting .
NEW QUESTION # 259
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
Answer: D
Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner References:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.
NEW QUESTION # 260
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
Explanation:
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
NEW QUESTION # 261
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