Practice Free AIF-C01 Exam Online Questions – Page 10

Question #91

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis.

The company wants to classify the sentiment of text passages as positive or negative.

Which prompt engineering strategy meets these requirements?

A . Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
B . Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C . Provide the new text passage to be classified without any additional context or examples.
D . Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

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Question #92

An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data.

What is the MOST likely cause of this issue?

A . The model is underfit.
B . The model requires prompt engineering.
C . The model is biased.
D . The model is overfit.

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Question #93

A company deploys a foundation model (FM). The company notices that the FM is producing answers to user-submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.

Which AWS solution will meet this requirement?

A . Amazon Bedrock Guardrails
B . Amazon Bedrock Agents
C . Amazon SageMaker Clarify
D . Amazon SageMaker Model Monitor

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Correct Answer: A
A

Explanation:

The verified answer is A. Amazon Bedrock Guardrails. The requirement is to prevent a deployed foundation model from returning answers about a specific unwanted topic: politics. AWS documentation states that Amazon Bedrock Guardrails provides configurable safeguards to help you build safe generative AI applications. It also explains that guardrails provide safety and privacy controls across foundation models and help detect and filter undesirable content in user inputs and model responses. Most importantly for this question, AWS specifically identifies denied topics as a guardrail capability. With denied topics, you can define a set of topics that are undesirable in the context of your application, and the filter helps block them if they are detected in user queries or model responses. That exactly matches the scenario because the company wants political questions blocked before answers are sent to users.

Amazon Bedrock Agents is incorrect because agents are used to build applications that can reason , orchestrate tasks, call APIs, and connect to knowledge sources or action groups. Agents do not primarily exist to block topic categories in model input or output. Amazon SageMaker Clarify is incorrect because AWS describes SageMaker Clarify as a service for fairness, model explainability, feature attribution, and bias detection. It is useful for understanding model behavior and detecting bias, but it is not the correct control for filtering political responses from a generative AI application.

Amazon SageMaker Model Monitor is also incorrect because it monitors deployed model quality, data quality, bias drift, and feature attribution drift. Monitoring can identify problems after deployment, but it does not directly enforce real-time content filtering for foundation model responses. The question asks for a solution that ensures political answers are not sent to users. AWS Bedrock Guardrails is purpose-built for that control by applying safeguards to FM inputs and outputs.

Question #94

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.

Which solution will meet these requirements?

A . Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
B . Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C . Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
D . Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

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Question #95

Which task describes a use case for intelligent document processing (IDP)?

A . Predict fraudulent transactions.
B . Personalize product offerings.
C . Analyze user feedback and perform sentiment analysis.
D . Automatically extract and format data from scanned files.

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Correct Answer: D
D

Explanation:

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.

Question #96

A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.

Which solution will meet these requirements?

A . Implement moderation APIs.
B . Retrain the model with a general public dataset.
C . Perform model validation.
D . Automate user feedback integration.

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Question #97

An AI practitioner is using an Amazon SageMaker notebook to train an ML prediction model for fraud detection. The company wants the model to be accurate for an unseen dataset.

Which two characteristics does the AI practitioner want the model to have?

A . High variance / high bias
B . High variance / low bias
C . Low variance / high bias
D . Low variance / low bias

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Question #98

An animation company wants to provide subtitles for its content.

Which AWS service meets this requirement?

A . Amazon Comprehend
B . Amazon Polly
C . Amazon Transcribe
D . Amazon Translate

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Question #99

A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company’s product manuals. The manuals are stored as PDF files.

Which solution meets these requirements MOST cost-effectively?

A . Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
B . Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
C . Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
D . Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

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Question #100

A company wants to generate synthetic data responses for multiple prompts from a large volume of data. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.

A . Input the prompts into the model. Generate responses by using real-time inference.
B . Use Amazon Bedrock batch inference. Generate responses asynchronously.
C . Use Amazon Bedrock agents. Build an agent system to process the prompts recursively.
D . Use AWS Lambda functions to automate the task. Submit one prompt after another and store each response.

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