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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Price: | $100 USD |
| Real Exam Qty: | 65 |
| Passing Score: | 700/1000 |
| Exam Format: | Multiple response, Multiple choice |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Available Languages: | Spanish, English, Portuguese (Brazil), Korean, Japanese, Simplified Chinese |
| Exam Duration: | 90 minutes |
| 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 # 326
A company needs to scan its Amazon EC2-based ML infrastructure for security vulnerabilities before deploying generative AI (GenAI) models.
Which AWS service provides automated vulnerability assessment?
Answer: A
Explanation:
The verified answer is C. Amazon Inspector. The company needs automated vulnerability assessment for Amazon EC2-based infrastructure. AWS documentation states that Amazon Inspector scans EC2 instances for package vulnerabilities and network reachability issues and produces findings. It compares instance metadata against vulnerability intelligence and security advisory rules. This is exactly the service used to assess EC2 infrastructure before deploying sensitive workloads such as generative AI models.
AWS also documents that Amazon Inspector scans EC2 instances for common vulnerabilities and exposures, operating system package vulnerabilities, programming language package vulnerabilities, network exposure issues, and network reachability issues. This confirms that Inspector is the correct service for automated vulnerability assessment of EC2-based ML infrastructure.
AWS CloudFormation is incorrect because it provisions infrastructure as code. It can create EC2 instances, security groups, and related resources, but it does not perform vulnerability assessment. Amazon Comprehend is incorrect because it is a natural language processing service used to extract insights from text. It does not scan infrastructure. AWS Trusted Advisor is incorrect because it provides best-practice recommendations across categories such as cost optimization, security, performance, and service limits, but it is not the dedicated automated vulnerability assessment service for EC2 instances.
The requirement is specific: scan EC2-based ML infrastructure for vulnerabilities before deployment. The AWS-native service that performs this function is Amazon Inspector.
NEW QUESTION # 327
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
Answer: A
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Explanation:
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
References:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 328
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Answer: B
Explanation:
* Anomaly detection identifies unusual behavior (such as suspicious IP traffic) compared to normal baselines.
* Speech recognition (A) is irrelevant.
* NER in NLP (B) extracts entities from text, not detect malicious IPs.
* Fraud forecasting (D) predicts fraudulent transactions but not directly suspicious IP activity.
# Reference:
AWS Documentation - Anomaly Detection
NEW QUESTION # 329
A company is building a custom AI solution in Amazon SageMaker Studio to analyze financial transactions for fraudulent activity in real time. The company needs to ensure that the connectivity from SageMaker Studio to Amazon Bedrock traverses the company's VPC.
Which solution meets these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
AWS PrivateLink enables private connectivity between AWS services through VPC endpoints, ensuring traffic does not traverse the public internet.
AWS guidance recommends PrivateLink for:
* Secure, private service access
* Regulatory and compliance requirements
* VPC-based architecture
Why the other options are incorrect:
* IAM (A) controls access, not network routing.
* Macie (B) is a data security service.
* VPC peering (D) is not required for Bedrock access.
AWS AI document references:
* Amazon Bedrock Networking and Security
* Private Connectivity with AWS PrivateLink
* Secure AI Architectures on AWS
NEW QUESTION # 330
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 331
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