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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple response, Multiple choice |
| Exam Duration: | 90 minutes |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Available Languages: | English, Korean, Simplified Chinese, Spanish, Portuguese (Brazil), Japanese |
| Real Exam Qty: | 65 |
| Exam Price: | $100 USD |
| Passing Score: | 700/1000 |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| 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 # 366
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: A
Explanation:
The verified answer is B. AWS Artifact. AWS Artifact provides on-demand downloads of AWS security and compliance documents, including compliance reports and certifications. AWS documentation also states that AWS Artifact can provide security and compliance documents for independent software vendors who sell products on AWS Marketplace. This directly matches the requirement involving ISV compliance reports.
AWS Artifact documentation further explains that users can access AWS reports on the AWS reports tab and access independent software vendor reports on the Third-party reports tab. This confirms that AWS Artifact is the correct service for obtaining ISV compliance documentation.
The notification requirement also points to AWS Artifact. AWS documentation states that the AWS Artifact console can be used to configure email notifications for updates on agreements and reports in AWS Artifact.
AWS Artifact sends those notifications through AWS User Notifications, and users can specify notification recipients and which notifications they receive.
AWS Audit Manager is incorrect because it helps automate evidence collection and compliance assessments for the customer's AWS usage. It does not primarily provide ISV compliance report notifications. AWS Trusted Advisor is incorrect because it provides best-practice recommendations for AWS accounts, not ISV report availability. AWS Data Exchange is incorrect because it is used to find, subscribe to, and use third- party data products. It is not the compliance report and notification portal.
Therefore, the correct service is AWS Artifact.
NEW QUESTION # 367
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code.
Which Amazon SageMaker feature meets this requirement?
Answer: B
Explanation:
The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
"Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience." This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B). SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C). SageMaker Model Monitor monitors model quality in production but doesn't build or train models.
D). SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide - AutoML and No-Code Tools Section AWS Machine Learning Blog: "Predict Employee Attrition with SageMaker Canvas"
NEW QUESTION # 368
A financial company is evaluating two AI models for loan approval decisions. A deep learning model shows higher predictive accuracy than a decision tree model. However, financial regulations require that loan approval decisions must be explainable to customers and auditors.
What should the company consider when selecting a model?
Answer: C
Explanation:
AWS responsible AI guidance emphasizes that predictive accuracy is not the only consideration when selecting an AI/ML model. In regulated applications such as financial lending, organizations may have explicit requirements to explain why a decision was made. This creates a tradeoff between predictive performance and model interpretability.
AWS Prescriptive Guidance states regarding tree models: "For single tree models, the split variables and leaf values provide an immediately explainable model." This is particularly important for a loan-approval scenario because stakeholders may need to determine which applicant characteristics influenced a rejection or approval.
AWS also explains that model interpretability can be a required business outcome in regulated industries, specifically including finance. Interpretability can help organizations justify important decisions, support fairness, satisfy auditing requirements, build trust, and investigate problematic predictions.
Deep neural networks can achieve excellent predictive performance, but their large numbers of parameters and nonlinear interactions generally make their internal decision-making more difficult for humans to interpret directly. Explainability techniques such as SHAP can provide feature attribution for complex models, but this is different from using a model whose decision structure is inherently straightforward.
A decision tree represents decisions through observable splits and branches. For example, an auditor can examine which features and thresholds produced a particular path through the tree. That makes the decision mechanism inherently easier to communicate and inspect.
Option A incorrectly assumes predictive accuracy implies explainability. Option C incorrectly separates complexity from interpretability. Option D is incorrect because different model classes can vary substantially in transparency even when solving the same business problem.
Therefore, where regulatory explainability is a mandatory requirement, the organization should explicitly consider the accuracy-versus-interpretability tradeoff, making B the correct answer.
NEW QUESTION # 369
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 # 370
A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?
Answer: C
NEW QUESTION # 371
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