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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Exam Format: | Multiple choice, Multiple response |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Passing Score: | 700/1000 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 65 |
| Certificate Validity Period: | 3 years |
| Available Languages: | Japanese, Simplified Chinese, English, Spanish, Korean, Portuguese (Brazil) |
| Exam Price: | $100 USD |
| 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 # 309
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:
Comprehensive and Detailed
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 # 310
A company wants to fine-tune a foundation model (FM) by using AWS services. The company needs to ensure that its data stays private, safe, and secure in the source AWS Region where the data is stored.
Which combination of steps will meet these requirements MOST cost-effectively? (Select TWO.)
Answer: B,C
Explanation:
The most cost-effective combination is to perform the managed foundation-model customization through Amazon Bedrock and secure connectivity by using Amazon VPC and AWS PrivateLink.
AWS documentation states that when a Bedrock model-customization job is submitted, Amazon Bedrock accesses the designated training and validation data to fine-tune the selected foundation model. The customization operation is available programmatically through the Amazon Bedrock API.
For network isolation, AWS states: "Using a VPC protects your data" and recommends creating an interface endpoint with AWS PrivateLink so the data does not need to be available over the public internet.
AWS PrivateLink establishes private connectivity between resources in the VPC and Amazon Bedrock without routing application traffic through the public internet. For model customization, AWS supports supplying a VPC configuration containing appropriate subnets and security groups to protect access to training data.
AWS also documents that Bedrock fine-tuning data is used only for the customization process and is not used to train the underlying base model for unrelated customers. Training and validation data provided for fine-tuning is not retained by Bedrock after the job completes.
Option A would introduce unnecessary on-premises infrastructure and hardware costs. Fine-tuning supported Bedrock foundation models does not require deploying the entire service through AWS Outposts.
Option D is invalid because customers do not host the managed Amazon Bedrock service API on premises.
Option E provides monitoring and observability, but CloudWatch logging does not establish private network connectivity or isolate model-customization data.
Thus, Amazon Bedrock supplies the managed fine-tuning capability while VPC connectivity and AWS PrivateLink provide the private network path. This combination minimizes infrastructure management while satisfying the stated security requirement.
Therefore, the correct answers are B and C.
NEW QUESTION # 311
A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?
Answer: A
Explanation:
Comprehensive and Detailed
Fine-tuning requires paired input-output examples to teach the model how to respond to inputs with desired styled outputs.
Single inputs (A) or outputs (B) are insufficient.
Separate, unpaired samples (D) don't establish the input-output mapping.
Reference:
AWS Documentation - Preparing data for fine-tuning FMs
NEW QUESTION # 312
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
Answer: A
Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
* B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
* A and C require manual effort, increasing operational overhead.
* D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
NEW QUESTION # 313
A company is building an AI application to automate business processes. The company uses a foundation model (FM) to support the application.
The company needs to select datasets to assess the quality of the AI model ' s behavior.
Which type of datasets will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI and generative AI evaluation guidance emphasizes that assessing the quality and behavior of a foundation model requires representative and diverse evaluation datasets. These datasets should reflect real-world usage patterns, edge cases, and multiple business scenarios to properly evaluate how the model behaves in production.
Using diverse datasets that cover various use cases and usage scenarios allows organizations to:
Evaluate robustness and generalization of the FM
Identify failure modes, bias, and unsafe behavior across different inputs Validate that the model performs consistently across business workflows Why the other options are incorrect:
A may remove meaningful real-world patterns and does not reflect realistic usage.
B risks reinforcing model biases and does not provide independent evaluation.
D does not reflect real or meaningful business scenarios and can distort evaluation results.
AWS AI Study Guide References:
AWS Responsible AI evaluation practices
AWS guidance on foundation model testing and validation
NEW QUESTION # 314
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