AIF-C01 Exam Questions Tutorials

Comprehensive and Detailed Explanation From AWS AI documents (with references):

Intelligent Document Processing (IDP) on AWS is focused on extracting structured information from unstructured or semi-structured documents (for example: scanned PDFs, images, forms, invoices, receipts, contracts) and then normalizing/structuring that extracted data so it can be stored, searched, validated, routed through workflows, or used downstream by analytics and applications.

Option D matches this exactly: “Automatically extract and format data from scanned files.” That is the core IDP outcome—turning document images into machine-readable, structured data (for example, fields like vendor name, invoice number, dates, totals, line items, key-value pairs, tables, and text).

Why the other options are not IDP:

A (fraud prediction) is typically a fraud detection / anomaly detection ML use case (often involving transaction streams, behavioral signals, and risk scoring), not document extraction.

B (personalization) is a recommendation/personalization use case (user-item interactions, segmentation, ranking).

C (sentiment analysis) is an NLP text analytics use case (classifying sentiment from text), which may use text derived from documents, but the primary definition of IDP is the document understanding + extraction + structuring pipeline.

Important note about “100% verified” + “exact extract”:

You asked for exact extracts from AWS AI documents and “100% verified answers.” In this chat, web browsing is disabled, and you did not provide the AWS document text (PDF/notes) to quote from—so I cannot truthfully provide verbatim excerpts from the official AWS documents. I can still give you the correct answer and an AWS-accurate explanation based on established AWS IDP definitions and services (notably Amazon Textract), and I can list the exact AWS document titles/sections you should use to copy the verbatim extract from.

AWS references to use for verbatim “exact extracts” (no URLs):

Amazon Textract Documentation — Overview / “What is Amazon Textract?” (describes extracting text and structured data such as forms and tables from scanned documents).

Intelligent Document Processing on AWS (AWS guidance material / solution guidance) — sections describing IDP as extracting, classifying, and structuring data from documents to automate business workflows.

AWS Machine Learning / AI Services Documentation — Text extraction and document understanding service descriptions (Textract positioned for document text + forms + tables extraction).

If you paste the specific AWS document paragraph(s) you’re using (or upload the doc text), I can quote the exact extract verbatim under each question exactly in the format you want, with precise citations to the relevant section/page within that document—without any external links.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

The correct answer is A – Data residency. AWS defines data residency as ensuring that regulated or sensitive data—such as patient medical records under healthcare laws—remains physically stored and processed within specific national or regional boundaries. This aligns with regulatory frameworks such as HIPAA, GDPR, and country-specific health data protection acts. AWS allows customers to deploy all ML workloads, including Amazon SageMaker, Amazon Bedrock, and Amazon S3, within a chosen AWS Region, ensuring no cross-border data movement unless explicitly configured. Data quality (B) refers to accuracy and consistency, discoverability (C) relates to cataloging, and enrichment (D) refers to enhancing datasets. None of these address the compliance requirement to prevent data from leaving the country. Data residency is a core component of AWS’s Shared Responsibility Model and foundational for healthcare AI compliance.

Referenced AWS Documentation:

AWS Data Privacy Whitepaper – Data Residency Controls

AWS Compliance Programs – Regional Data Handling Requirements

To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.

A. Include Fairness Metrics for Model Evaluation:

Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.

These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.

C. Modify the Training Data to Mitigate Bias:

Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.

Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.

Why Other Options are Incorrect:

B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.

D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.

E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.