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>> AIF-C01 Questions Answers <<
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NEW QUESTION # 23
A company needs to use Amazon SageMaker AI for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.
Which solution will meet these requirements?
Answer: D
Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
"When you enable network isolation, your model can't make any outbound network calls. This is useful for security and regulatory compliance when working with sensitive data."
NEW QUESTION # 24
What does an F1 score measure in the context of foundation model (FM) performance?
Answer: D
Explanation:
The F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true positive predictions among all positive predictions made by the model), while recall measures the model's ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of model performance but are not related to the F1 score.
NEW QUESTION # 25
A company has a generative AI application that uses a pre-trained foundation model (FM) on Amazon Bedrock. The company wants the FM to include more context by using company information.
Which solution meets these requirements MOST cost-effectively?
Answer: A
Explanation:
Amazon Bedrock Knowledge Bases enable Retrieval Augmented Generation (RAG) by letting you connect external company data sources to your foundation model in a serverless, cost-effective manner-without retraining or fine-tuning. This allows the model to answer questions and generate content grounded in your own documents or data.
A is correct:
"Knowledge Bases for Amazon Bedrock enable generative AI applications to retrieve and include your company's information for more contextual responses, without the need for expensive retraining or custom models." (Reference: Amazon Bedrock Knowledge Bases) B is not cost-effective or guaranteed to add your company's context.
C (Agents) are for orchestrating workflows, not specifically RAG/context.
D (Custom model deployment) is costly and unnecessary for just adding context.
NEW QUESTION # 26
A company has an ML model. The company wants to know how the model makes predictions. Which term refers to understanding model predictions?
Answer: A
Explanation:
The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.
From AWS documentation:
"Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed." This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.
Explanation of other options:
B). Model training refers to the process of teaching a model from data and doesn't explain how predictions are made.
C). Model interoperability refers to the ability of systems or models to work across different platforms or environments.
D). Model performance refers to how accurate or effective the model is but doesn't relate to the explanation of its decisions.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon SageMaker Clarify Documentation - Explainability and Bias Detection
* AWS Machine Learning Specialty Study Guide - Responsible AI and Model Explainability
NEW QUESTION # 27
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: C
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 Reference:
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 # 28
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