Amazon Web Services Updated AIF-C01 Exam Questions and Answers by diego

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens: Would increase costs, not lower them.

D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Amazon Macie uses machine learning to:

Discover sensitive data such as PII

Classify data stored in Amazon S3

Help prevent unintended data exposure

AWS security guidance recommends Macie before data sharing to ensure compliance and privacy protection.

Why the other options are incorrect:

Kendra (A) is a search service.

Textract (C) extracts text from documents.

Data Exchange (D) shares datasets, not analyzes sensitivity.

AWS AI document references:

Amazon Macie Overview

Protecting Sensitive Data in S3

Data Privacy and Governance on AWS

AWS responsible AI guidance emphasizes that predictive accuracy is not the only consideration when selecting an AI/ML model. In regulated applications such as financial lending, organizations may have explicit requirements to explain why a decision was made. This creates a tradeoff between predictive performance and model interpretability.

AWS Prescriptive Guidance states regarding tree models: “For single tree models, the split variables and leaf values provide an immediately explainable model.” This is particularly important for a loan-approval scenario because stakeholders may need to determine which applicant characteristics influenced a rejection or approval.

AWS also explains that model interpretability can be a required business outcome in regulated industries, specifically including finance. Interpretability can help organizations justify important decisions, support fairness, satisfy auditing requirements, build trust, and investigate problematic predictions.

Deep neural networks can achieve excellent predictive performance, but their large numbers of parameters and nonlinear interactions generally make their internal decision-making more difficult for humans to interpret directly. Explainability techniques such as SHAP can provide feature attribution for complex models, but this is different from using a model whose decision structure is inherently straightforward.

A decision tree represents decisions through observable splits and branches. For example, an auditor can examine which features and thresholds produced a particular path through the tree. That makes the decision mechanism inherently easier to communicate and inspect.

Option A incorrectly assumes predictive accuracy implies explainability. Option C incorrectly separates complexity from interpretability. Option D is incorrect because different model classes can vary substantially in transparency even when solving the same business problem.

Therefore, where regulatory explainability is a mandatory requirement, the organization should explicitly consider the accuracy-versus-interpretability tradeoff, making B the correct answer .

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The verified answer is C. Add unified governance, discovery, and collaboration capabilities to Amazon SageMaker AI. The question is not asking only for storage, monitoring, or a basic repository. It asks for a scalable way for employees to find, access, and properly use company data while remaining compliant with policies and standards. AWS documentation for the next generation of Amazon SageMaker states that it simplifies discovery, governance, and collaboration for data and AI. Amazon SageMaker Catalog helps users securely discover and access approved data and assets through semantic search and governance workflows.

This directly matches the company’s requirement. In a rapidly growing multinational organization, data becomes harder to locate, understand, govern, and reuse. Unified governance and catalog capabilities allow teams to publish, discover, request access to, and collaborate on approved assets. AWS also describes Amazon SageMaker Catalog as a way to discover, govern, and collaborate on structured data, unstructured data, AI models, BI dashboards, and applications from a single catalog.

Option A is incorrect because a repository without integration does not solve governance, collaboration, or policy-based access. Option B is incomplete because storage alone does not help employees find or properly use data. Option D is also incomplete because monitoring can detect issues, but it does not provide data discovery, governance workflows, or collaboration features.

The correct solution must combine discovery, access governance, metadata, collaboration, and policy-aligned usage. Therefore, option C is the correct answer.