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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 |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple response, Multiple choice |
| Available Languages: | Spanish, Simplified Chinese, Korean, Portuguese (Brazil), Japanese, English |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700/1000 |
| Exam Price: | $100 USD |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Training and Certification Registration AWS Certification Portal |
| 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 # 335
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?
Answer: D
Explanation:
The correct answer is C because Amazon Nova Reel is the AWS foundation model designed for generative video use cases, providing end-to-end video generation using generative AI, which significantly reduces video creation time and eliminates the need for manual assembly.
According to AWS Bedrock documentation:
" Amazon Nova Reel enables users to generate short-form video content directly from prompts, including the ability to define style, motion, scenes, and transitions - streamlining the generative content creation process.
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This is the most operationally efficient choice as it does not require stitching together images or using external editing tools.
Explanation of other options:
A and B involve generating intermediate images and then manually creating videos using video editing tools
- not operationally efficient.
D). Amazon Nova Pro is intended for high-end professional-grade image or 3D content generation, but not specifically optimized for video generation like Nova Reel.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Directory - Nova Models Overview
AWS GenAI Foundation Model Comparison Guide
AWS Generative AI for Creators Whitepaper (2024)
NEW QUESTION # 336
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.
Answer: B
Explanation:
* With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
* Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
* Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
# Reference:
AWS Shared Responsibility Model
NEW QUESTION # 337
What does an F1 score measure in the context of foundation model (FM) performance?
Answer: B
Explanation:
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. Reference: AWS Foundation Models Guide.
NEW QUESTION # 338
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: D
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 # 339
A company plans to build an AI model for the company's global customer base. The company wants to train the model on a dataset that reflects user diversity.
Which action will meet this requirement?
Answer: D
Explanation:
The verified answer is A. Balance class representation in the dataset . The requirement is to train an AI model on data that reflects the diversity of a global customer base. In AWS responsible AI guidance, dataset quality is directly connected to fairness, bias reduction, and model behavior across different user groups. AWS identifies dataset characteristics such as inclusivity, diversity, curated data sources, and balanced datasets as part of responsible AI knowledge for AI practitioners. This directly supports the need for a dataset that represents diverse users instead of overrepresenting one group and underrepresenting another.
AWS also explains that bias can result from imbalances in data or from differences in model performance across groups. Amazon SageMaker Clarify is designed to help detect and mitigate potential bias during data preparation, after training, and in deployed models by examining specified attributes. This supports the principle that the training dataset should be examined and balanced before model training so that the model does not preferentially learn patterns from majority groups while performing poorly for minority groups.
Option B is incorrect because using a regional dataset, even if complete, does not satisfy a global diversity requirement. A complete regional dataset may still exclude languages, demographics, cultural contexts, behaviors, and usage patterns from other regions. Option C is incorrect because oversampling majority class data worsens imbalance. It increases the dominance of already overrepresented groups and can amplify biased model behavior. Option D is also incorrect because dropping minority class records removes exactly the user groups that must be represented for a global customer base.
AWS SageMaker Clarify documentation specifically describes class imbalance bias as a situation where one facet has fewer training samples than another. AWS notes that models may preferentially fit larger facets at the expense of smaller facets, leading to higher training error for underrepresented groups. Therefore, balancing class representation is the best action because it improves dataset representativeness and supports responsible AI fairness for a diverse global user base.
NEW QUESTION # 340
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