Practice Free AIF-C01 Exam Online Questions
Question #41
What is the benefit of fine-tuning a foundation model (FM)?
- A . Fine-tuning reduces the FM’s size and complexity and enables slower inference.
- B . Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.
- C . Fine-tuning keeps the FM’s knowledge up to date by pre-training the FM on more recent data.
- D . Fine-tuning improves the performance of the FM on a specific task by further training the FM on
new labeled data.
Correct Answer: D
D
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain-specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).
From AWS Docs:
“With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases.”
“Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain.”
Reference: AWS Documentation C Fine-tuning foundation models in Amazon Bedrock
D
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain-specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).
From AWS Docs:
“With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases.”
“Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain.”
Reference: AWS Documentation C Fine-tuning foundation models in Amazon Bedrock
Question #42
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
- A . Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
- B . Add a role description to the prompt context that instructs the model of the age range that the response should target.
- C . Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
- D . Summarize the response text depending on the age of the user so that younger users receive shorter responses.
Correct Answer: B
B
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user’s age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner
Reference: Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
B
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user’s age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner
Reference: Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
Question #43
Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?
- A . Expanding initiatives across business units to create long-term business value
- B . Ensuring alignment with business standards, revenue goals, and stakeholder expectations
- C . Overcoming challenges to drive business transformation and growth
- D . Developing policies and guidelines for data, transparency, responsible AI, and compliance
Correct Answer: D
D
Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory
requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed
Option A: Expanding initiatives across business units to create long-term business value While expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectations Alignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growth Overcoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and compliance This is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference: AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/)
AWS AI Practitioner Learning Path: Module on AI Governance
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
D
Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory
requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed
Option A: Expanding initiatives across business units to create long-term business value While expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectations Alignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growth Overcoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and compliance This is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference: AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/)
AWS AI Practitioner Learning Path: Module on AI Governance
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
Question #44
A company wants to set up private access to Amazon Bedrock APIs from the company’s AWS account.
The company also wants to protect its data from internet exposure.
- A . Use Amazon CloudFront to restrict access to the company’s private content
- B . Use AWS Glue to set up data encryption across the company’s data catalog
- C . Use AWS Lake Formation to manage centralized data governance and cross-account data sharing
- D . Use AWS PrivateLink to configure a private connection between the company’s VPC and Amazon Bedrock
Correct Answer: D
D
Explanation:
AWS PrivateLink enables private connectivity between your VPC and supported AWS services (like Amazon Bedrock) without sending traffic over the public internet.
CloudFront (A) is for CDN and content delivery, not private service connections.
AWS Glue (B) is for ETL/data catalog, not networking.
Lake Formation (C) provides governance for data lakes, not API network isolation.
Reference: AWS Documentation C Access Amazon Bedrock with PrivateLink
D
Explanation:
AWS PrivateLink enables private connectivity between your VPC and supported AWS services (like Amazon Bedrock) without sending traffic over the public internet.
CloudFront (A) is for CDN and content delivery, not private service connections.
AWS Glue (B) is for ETL/data catalog, not networking.
Lake Formation (C) provides governance for data lakes, not API network isolation.
Reference: AWS Documentation C Access Amazon Bedrock with PrivateLink
Question #45
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?
- A . One-shot prompting
- B . Prompt chaining
- C . Tree of thoughts
- D . Retrieval Augmented Generation (RAG)
Correct Answer: C
Question #46
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.
Correct Answer: B
B
Explanation:
The correct answer is B C Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide C Batch Inference
AWS ML Specialty Study Guide C Scalable Inference Options
B
Explanation:
The correct answer is B C Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide C Batch Inference
AWS ML Specialty Study Guide C Scalable Inference Options
Question #47
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.
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.
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 #48
A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?
- A . Measurement bias
- B . Sampling bias
- C . Observer bias
- D . Confirmation bias
Correct Answer: B
B
Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
B
Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
Question #49
A company wants to fine-tune a foundation model (FM) for a specific use case. The company needs to deploy the FM on Amazon Bedrock for internal use.
Which solution will meet these requirements?
- A . Run responses that have been generated by a pre-trained FM through Amazon Bedrock Guardrails to create the custom FM.
- B . Use Amazon Personalize to customize the FM with custom data.
- C . Use conversational builder for Amazon Bedrock Agents to create the custom model.
- D . Use Amazon SageMaker AI to customize the FM. Then, import the trained model into Amazon Bedrock.
Correct Answer: D
D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock supports importing custom foundation models that have been trained or fine-tuned outside of Bedrock, including models customized using Amazon SageMaker AI.
Amazon SageMaker AI provides:
Full control over model training and fine-tuning
Ability to train models using approved internal datasets Advanced customization beyond prompt-based techniques
After customization in SageMaker, the trained model can be imported into Amazon Bedrock for managed, scalable inference and internal deployment.
Why the other options are incorrect:
A (Guardrails) enforce safety, compliance, and output controls; they do not create or fine-tune models.
B (Amazon Personalize) is a recommendation service, not a foundation model customization tool.
C (Agents) orchestrate tasks and tool usage but do not modify or fine-tune model weights.
AWS AI document references:
Amazon Bedrock Documentation ― section on Custom model import
Amazon SageMaker AI Overview ― section on model training and fine-tuning
Foundation Models on AWS ― customization approaches
D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Bedrock supports importing custom foundation models that have been trained or fine-tuned outside of Bedrock, including models customized using Amazon SageMaker AI.
Amazon SageMaker AI provides:
Full control over model training and fine-tuning
Ability to train models using approved internal datasets Advanced customization beyond prompt-based techniques
After customization in SageMaker, the trained model can be imported into Amazon Bedrock for managed, scalable inference and internal deployment.
Why the other options are incorrect:
A (Guardrails) enforce safety, compliance, and output controls; they do not create or fine-tune models.
B (Amazon Personalize) is a recommendation service, not a foundation model customization tool.
C (Agents) orchestrate tasks and tool usage but do not modify or fine-tune model weights.
AWS AI document references:
Amazon Bedrock Documentation ― section on Custom model import
Amazon SageMaker AI Overview ― section on model training and fine-tuning
Foundation Models on AWS ― customization approaches
Question #50
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
- A . Data pre-processing
- B . Feature engineering
- C . Exploratory data analysis
- D . Hyperparameter tuning
Correct Answer: C
C
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data’s existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner
Reference: Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
C
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data’s existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner
Reference: Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.