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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | USD 100 |
| Exam Duration: | 90 minutes |
| Passing Score: | 700 / 1000 |
| Real Exam Qty: | 80 |
| Available Languages: | Simplified Chinese, English, Japanese, Portuguese (Brazil), Korean |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple response, Multiple choice |
| 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 # 239
Which option is an example of unsupervised learning?
Answer: B
Explanation:
Comprehensive and Detailed
Unsupervised learning involves discovering hidden patterns without labeled data. Example: clustering.
Image recognition (B) is supervised learning.
House price prediction (C) is regression (supervised).
NEW QUESTION # 240
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?
Answer: B
Explanation:
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production.
This option does not fully meet the requirements.
Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality.
Building a new model may not be necessary if retraining can address the issue.
Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 241
A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully. The company needs to prevent the model from including specific items in the generated images.
Which solution will meet this requirement?
Answer: C
Explanation:
The correct answer is C - Use a negative prompt. Negative prompts instruct a generative image model to avoid certain features, objects, or styles in the output. This technique is fully supported by models like Amazon Nova Canvas on Bedrock, which are based on diffusion or image generation architectures. According to AWS documentation, negative prompts refine output control by telling the model what not to include, thereby improving brand alignment, compliance, or creative direction. A higher temperature increases randomness, not control. A detailed prompt helps, but without exclusion instructions, the model may still include unwanted elements. Changing the model may yield better output but doesn't directly solve this control requirement. Negative prompts are purpose-built for this scenario.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - Prompt Engineering for Image Models
AWS Generative AI Guide - Controlled Generation with Negative Prompts
NEW QUESTION # 242
A company used Amazon Bedrock to build an AI assistant. The AI assistant received a user prompt that instructed the assistant to provide the guidelines that the company used to configure the assistant. The prompt also instructed the assistant to display the full text of the initial setup. The AI assistant revealed its system prompt in the response.
Which type of prompt attack has occurred?
Answer: C
Explanation:
The attack is specifically prompt leakage because the user ' s objective is to obtain confidential system-level instructions or configuration information from the AI application. AWS defines prompt leakage as user prompts designed to "extract or reveal the system prompt, developer instructions, or other confidential configuration details." The scenario precisely matches this definition. The user asks the assistant to disclose the guidelines used to configure it and to reproduce the complete initial setup. The assistant then reveals its system prompt. The defining outcome is therefore unauthorized disclosure of protected prompt instructions.
Prompt leakage is related to the broader category of prompt attacks , and a leakage attempt can involve prompt-injection techniques. However, when AWS asks for the specific type of attack and the attacker is intentionally trying to expose the hidden system prompt or developer instructions, prompt leakage is the more precise classification.
A jailbreak generally attempts to bypass model safeguards or restrictions so the model performs behavior that would normally be prohibited. Although jailbreaks and prompt leakage can overlap, the core purpose in this scenario is disclosure of system configuration rather than simply bypassing a safety policy.
Prompt injection is the broader technique of introducing malicious instructions designed to override or manipulate existing instructions. For example, an attacker might tell the application to ignore previous instructions. AWS includes jailbreaks, prompt injections, and prompt leakages within its prompt-attack filtering capabilities.
A denied topic violation occurs when content concerns a topic explicitly configured as prohibited in Amazon Bedrock Guardrails. It does not describe system-prompt disclosure.
AWS recommends protecting generative AI applications by using Bedrock Guardrails prompt-attack detection, clearly defined system instructions, controlled access to tools and resources, input evaluation, output validation, and security testing.
Because confidential system instructions were explicitly requested and subsequently disclosed, the exact AWS-aligned classification is B. Prompt leakage .
NEW QUESTION # 243
A company has fine-tuned an Amazon Bedrock foundation model (FM) to produce short document summaries. The company wants an automated metric that compares each model-generated summary with its human-written reference summary.
Which metric will meet these requirements?
Answer: C
Explanation:
AWS documentation identifies ROUGE as a standard evaluation metric for text summarization tasks.
ROUGE measures the overlap of n-grams, sequences, or word pairs between a machine-generated summary and a human-written reference summary, making it ideal for automated evaluation.
In this scenario, the company needs a metric that directly compares generated summaries with human references. ROUGE scores quantify how much of the reference content is captured by the model output, which aligns with AWS recommendations for evaluating summarization quality.
The other metrics are not appropriate. F1 score is typically used for classification tasks. Perplexity measures language model confidence but does not compare outputs to reference summaries. Frechet Inception Distance is used for image generation tasks, not text.
AWS consistently recommends ROUGE for summarization evaluation, making it the correct choice.
NEW QUESTION # 244
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