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>> Examcollection AIF-C01 Questions Answers <<
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NEW QUESTION # 289
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.
Which option represents the model's problem?
Answer: A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
* Interpretability refers to understanding how a model arrives at its predictions.
* Nondeterminism refers to variation in outputs across runs due to probabilistic sampling.
* Accuracy is a general performance metric, not the specific phenomenon described.
AWS AI Study Guide References:
* AWS generative AI challenges and limitations
* AWS guidance on hallucinations in foundation models
NEW QUESTION # 290
A company wants to build a customer-facing generative AI application. The application must block or mask sensitive information. The application must also detect hallucinations.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends using managed, purpose-built services to enforce safety, compliance, and responsible AI controls in generative AI applications in order to minimize operational complexity and maintenance effort.
Amazon Bedrock Guardrails are specifically designed to help customers:
Block or mask sensitive information, such as personally identifiable information (PII) Detect and reduce hallucinations by enforcing grounding and response constraints Apply content filters, topic restrictions, and safety policies consistently across generative AI applications Configure safeguards without building or managing custom infrastructure Because Guardrails are fully managed and integrated directly with Amazon Bedrock, they require minimal setup, no custom code for policy enforcement, and no infrastructure management, resulting in the least operational overhead.
Why the other options are less suitable:
A). AWS Lambda policy evaluator requires custom logic, testing, monitoring, and ongoing maintenance.
B). FM default policies alone are insufficient because they do not provide application-specific masking, hallucination detection, or configurable governance controls.
D). Custom EC2-based policy evaluators introduce the highest operational overhead due to server management, scaling, patching, and monitoring.
AWS AI Study Guide References:
Amazon Bedrock overview and safety features
Amazon Bedrock Guardrails for responsible generative AI
AWS best practices for building secure and governed generative AI applications
NEW QUESTION # 291
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?
Answer: C
Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.
NEW QUESTION # 292
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
Answer: A
Explanation:
AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.
* Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.
* Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.
* Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.
* Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no- internet-traffic policy.
AWS AI Practitioner References:
* AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.
NEW QUESTION # 293
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.
Which methodology should the company use to meet these requirements?
Answer: A
NEW QUESTION # 294
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