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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Duration: | 90 minutes |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Exam Price: | $100 USD |
| Real Exam Qty: | 65 |
| Available Languages: | Simplified Chinese, Korean, Japanese, English, Spanish, Portuguese (Brazil) |
| Exam Format: | Multiple response, Multiple choice |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700/1000 |
| Recommended Training: | AWS Certified AI Practitioner Official Exam Guide AWS Skill Builder - AI Practitioner Learning Plan |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 80
A metal manufacturing company wants to capture images of finished products on its production line. The company wants to automatically detect subtle surface defects such as scratches, dents, and texture inconsistencies.
Which approach will meet these requirements?
Answer: B
Explanation:
Computer vision is a field of artificial intelligence that enables machines to analyze and interpret visual information from images and videos.
In manufacturing, computer vision models can inspect product images to identify abnormalities, including scratches, dents, irregular surfaces, and other quality defects.
Deep learning approaches, including convolutional neural networks, can learn visual characteristics associated with acceptable and defective products.
AWS describes industrial computer vision architectures that use image analysis to automate manufacturing quality inspection and defect detection.
Natural language processing analyzes human language rather than visual surfaces.
Rule-based AI relies on explicitly defined conditions and can struggle with complex visual variations. Time- series forecasting models analyze sequential observations to predict future values.
Therefore, computer vision is the most appropriate approach for detecting subtle surface defects from manufacturing images.
NEW QUESTION # 81
A company is building a generative AI application with a foundation model (FM). The application needs to automatically generate marketing emails. The company wants the application's output text to be creative and short in length.
Which configuration of inference parameters will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Inference parameters control output behavior:
* Higher temperature increases creativity and randomness
* Shorter response length limits output size
AWS prompt and inference guidance recommends increasing temperature for creative tasks and reducing response length for concise outputs.
Why the other options are incorrect:
* A reduces creativity.
* B produces long outputs.
* D produces deterministic and lengthy responses.
AWS AI document references:
* Inference Parameters for Foundation Models
* Controlling Creativity and Length in Text Generation
* Prompt Engineering Best Practices
NEW QUESTION # 82
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.
Answer:
Explanation:
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)
NEW QUESTION # 83
A company wants to upload customer service email messages to Amazon S3 to develop a business analysis application. The messages sometimes contain sensitive data. The company wants to receive an alert every time sensitive information is found.
Which solution fully automates the sensitive information detection process with the LEAST development effort?
Answer: C
Explanation:
The correct answer is A because Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically detect sensitive data such as PII (personally identifiable information) in Amazon S3. It requires no custom development, and it can be configured to generate alerts when sensitive data is detected in newly uploaded objects.
From AWS documentation:
"Amazon Macie automatically discovers and classifies sensitive data in S3 buckets and generates alerts when it detects sensitive content, such as names, addresses, and credit card numbers." Explanation of other options:
B). Deploying an LLM on SageMaker to perform redaction is custom and operationally intensive.
C). Regex-based detection is brittle and requires extensive manual work, with high maintenance overhead.
D). Asking customers to avoid sharing sensitive data is not enforceable and does not meet compliance or security standards.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Macie Documentation - Sensitive Data Discovery and Alerting
* AWS Security Best Practices - Data Privacy and Governance
* AWS ML Specialty Guide - Governance and Compliance Automation
NEW QUESTION # 84
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: A
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 # 85
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