Practice Free AIF-C01 Exam Online Questions
Question #51
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
- A . Create a prompt template that teaches the LLM to detect attack patterns.
- B . Increase the temperature parameter on invocation requests to the LLM.
- C . Avoid using LLMs that are not listed in Amazon SageMaker.
- D . Decrease the number of input tokens on invocations of the LLM.
Correct Answer: A
A
Explanation:
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
Prompt Templates for Security:
A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
Why Option A is Correct:
Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
Why Other Options are Incorrect:
B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.
A
Explanation:
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
Prompt Templates for Security:
A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
Why Option A is Correct:
Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
Why Other Options are Incorrect:
B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.
Question #52
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company’s data.
Which strategy will successfully fine-tune the model?
- A . Provide labeled data with the prompt field and the completion field.
- B . Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
- C . Purchase Provisioned Throughput for Amazon Bedrock.
- D . Train the model on journals and textbooks.
Correct Answer: A
A
Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
Fine-Tuning Strategy:
To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
This allows the model to learn the desired behavior or response style based on the provided
examples.
Why Option A is Correct:
Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
Customization: Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.
Why Other Options are Incorrect:
B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
D. Train on journals and textbooks: Lacks the specific format and labeling required for fine-tuning.
A
Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
Fine-Tuning Strategy:
To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
This allows the model to learn the desired behavior or response style based on the provided
examples.
Why Option A is Correct:
Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
Customization: Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.
Why Other Options are Incorrect:
B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
D. Train on journals and textbooks: Lacks the specific format and labeling required for fine-tuning.
Question #53
A company wants to extract key insights from large policy documents to increase employee efficiency.
- A . Regression
- B . Clustering
- C . Summarization
- D . Classification
Correct Answer: C
C
Explanation:
Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.
Regression predicts numerical values.
Clustering groups similar items.
Classification assigns data into predefined categories.
Reference: AWS NLP Use Cases C Summarization
C
Explanation:
Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.
Regression predicts numerical values.
Clustering groups similar items.
Classification assigns data into predefined categories.
Reference: AWS NLP Use Cases C Summarization
Question #54
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.
Which option represents the model’s problem?
- A . Interpretability
- B . Nondeterminism
- C . Hallucinations
- D . Accuracy
Correct Answer: C
C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
Interpretability refers to understanding how a model arrives at its predictions. Nondeterminism refers to variation in outputs across runs due to probabilistic sampling. Accuracy is a general performance metric, not the specific phenomenon described. AWS AI Study Guide
Reference: AWS generative AI challenges and limitations
AWS guidance on hallucinations in foundation models
C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI documentation defines hallucinations as a condition in which a generative model produces outputs that appear fluent, confident, and plausible but are factually incorrect or not grounded in the training data or provided context.
Limited or insufficient training data increases the likelihood of hallucinations because the model lacks enough factual grounding to generate reliable responses. This behavior is a well-known challenge in large language models and foundation models.
Why the other options are incorrect:
Interpretability refers to understanding how a model arrives at its predictions. Nondeterminism refers to variation in outputs across runs due to probabilistic sampling. Accuracy is a general performance metric, not the specific phenomenon described. AWS AI Study Guide
Reference: AWS generative AI challenges and limitations
AWS guidance on hallucinations in foundation models
Question #55
A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in.
Which data governance strategy will ensure compliance and protect patient privacy?
- A . Data residency
- B . Data quality
- C . Data discoverability
- D . Data enrichment
Correct Answer: A
A
Explanation:
The correct answer is Data residency, which ensures that data remains stored and processed within specific geographical or jurisdictional boundaries. AWS defines data residency as the practice of keeping sensitive or regulated data, such as healthcare records, inside designated regions to meet local privacy laws like HIPAA or GDPR. Amazon SageMaker, Bedrock, and other AWS services allow region-specific resource deployment, guaranteeing data never leaves the country. Data quality refers to accuracy and consistency, while discoverability and enrichment concern accessibility and augmentation, not compliance. Data residency is central to AWS’s Shared Responsibility Model, ensuring organizations maintain sovereignty over healthcare data.
Referenced AWS AI/ML Documents and Study Guides:
AWS Data Privacy Whitepaper C Data Residency and Compliance AWS ML Specialty Guide C Data Governance and Security
A
Explanation:
The correct answer is Data residency, which ensures that data remains stored and processed within specific geographical or jurisdictional boundaries. AWS defines data residency as the practice of keeping sensitive or regulated data, such as healthcare records, inside designated regions to meet local privacy laws like HIPAA or GDPR. Amazon SageMaker, Bedrock, and other AWS services allow region-specific resource deployment, guaranteeing data never leaves the country. Data quality refers to accuracy and consistency, while discoverability and enrichment concern accessibility and augmentation, not compliance. Data residency is central to AWS’s Shared Responsibility Model, ensuring organizations maintain sovereignty over healthcare data.
Referenced AWS AI/ML Documents and Study Guides:
AWS Data Privacy Whitepaper C Data Residency and Compliance AWS ML Specialty Guide C Data Governance and Security
Question #56
Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?
- A . Reinforcement learning (RL)
- B . Natural language processing (NLP)
- C . Retrieval Augmented Generation (RAG)
- D . Transfer learning
Correct Answer: C
C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Retrieval Augmented Generation (RAG) enhances LLM responses by:
Retrieving relevant information from external knowledge sources
Injecting retrieved content into the prompt context
Reducing hallucinations and improving factual accuracy
AWS generative AI guidance describes RAG as a best practice when models must use up-to-date or domain-specific knowledge that is not embedded in the model weights.
Why the other options are incorrect:
RL (A) focuses on reward-based learning.
NLP (B) is a broad field, not a specific technique.
Transfer learning (D) adapts model weights but does not retrieve external data at inference time.
AWS AI document references:
Retrieval Augmented Generation on AWS
Improving LLM Accuracy with External Knowledge
Generative AI Architectures
C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Retrieval Augmented Generation (RAG) enhances LLM responses by:
Retrieving relevant information from external knowledge sources
Injecting retrieved content into the prompt context
Reducing hallucinations and improving factual accuracy
AWS generative AI guidance describes RAG as a best practice when models must use up-to-date or domain-specific knowledge that is not embedded in the model weights.
Why the other options are incorrect:
RL (A) focuses on reward-based learning.
NLP (B) is a broad field, not a specific technique.
Transfer learning (D) adapts model weights but does not retrieve external data at inference time.
AWS AI document references:
Retrieval Augmented Generation on AWS
Improving LLM Accuracy with External Knowledge
Generative AI Architectures
Question #57
Which option is a disadvantage of using generative AI models in production systems?
- A . Possible high accuracy and reliability
- B . Deterministic and consistent behavior
- C . Negligible computational resource requirements
- D . Hallucinations and inaccuracies
Correct Answer: D
D
Explanation:
AWS documentation identifies hallucinations and inaccuracies as a key challenge when deploying generative AI models in production environments. Hallucinations occur when a model generates responses that are plausible-sounding but factually incorrect, unsupported, or misleading.
Generative AI models are probabilistic by nature and do not have an inherent understanding of truth. AWS emphasizes that these models generate outputs based on patterns learned from training data, which can lead to confident but incorrect responses, especially when prompts lack sufficient context or when the model is asked about information outside its training scope.
The other options do not represent disadvantages. High accuracy and reliability are desired outcomes, not limitations. Deterministic behavior is not typical of generative models and is not a disadvantage. Negligible computational requirements are incorrect, as generative models typically require significant compute resources.
AWS recommends mitigation strategies such as Retrieval Augmented Generation, human review, prompt engineering, and output validation to reduce hallucinations. Nevertheless, hallucinations remain a known risk, making this option the correct answer.
D
Explanation:
AWS documentation identifies hallucinations and inaccuracies as a key challenge when deploying generative AI models in production environments. Hallucinations occur when a model generates responses that are plausible-sounding but factually incorrect, unsupported, or misleading.
Generative AI models are probabilistic by nature and do not have an inherent understanding of truth. AWS emphasizes that these models generate outputs based on patterns learned from training data, which can lead to confident but incorrect responses, especially when prompts lack sufficient context or when the model is asked about information outside its training scope.
The other options do not represent disadvantages. High accuracy and reliability are desired outcomes, not limitations. Deterministic behavior is not typical of generative models and is not a disadvantage. Negligible computational requirements are incorrect, as generative models typically require significant compute resources.
AWS recommends mitigation strategies such as Retrieval Augmented Generation, human review, prompt engineering, and output validation to reduce hallucinations. Nevertheless, hallucinations remain a known risk, making this option the correct answer.
Question #58
A company wants to use an AI model to generate labels for online news articles that the company publishes. The company selects a foundation model (FM) instead of a conventional ML model for this task.
What is one advantage of using an FM instead of a conventional ML model to meet this requirement?
- A . An FM does not require training.
- B . An FM is smaller and faster.
- C . An FM is more transparent.
- D . An FM is not biased.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
According to AWS Generative AI and Foundation Model guidance, foundation models are pre-trained on very large and diverse datasets, enabling them to perform a wide range of tasks such as classification, summarization, labeling, and content generation without task-specific training.
For labeling online news articles, a foundation model can be used out of the box or with minimal prompt engineering, whereas a conventional ML model would typically require:
Task-specific labeled training data
Model training and validation cycles
Ongoing retraining as content evolves
Why the other options are incorrect:
B. Smaller and faster C Foundation models are generally larger, not smaller, than conventional ML models.
C. More transparent C FMs are often less transparent due to their size and complexity.
D. Not biased C Foundation models can still inherit biases from training data; bias mitigation is required.
AWS AI Study Guide
Reference: AWS Generative AI fundamentals
AWS Foundation Model characteristics and benefits
A
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
According to AWS Generative AI and Foundation Model guidance, foundation models are pre-trained on very large and diverse datasets, enabling them to perform a wide range of tasks such as classification, summarization, labeling, and content generation without task-specific training.
For labeling online news articles, a foundation model can be used out of the box or with minimal prompt engineering, whereas a conventional ML model would typically require:
Task-specific labeled training data
Model training and validation cycles
Ongoing retraining as content evolves
Why the other options are incorrect:
B. Smaller and faster C Foundation models are generally larger, not smaller, than conventional ML models.
C. More transparent C FMs are often less transparent due to their size and complexity.
D. Not biased C Foundation models can still inherit biases from training data; bias mitigation is required.
AWS AI Study Guide
Reference: AWS Generative AI fundamentals
AWS Foundation Model characteristics and benefits
Question #59
An online learning company with large volumes of educational materials wants to use enterprise search.
Which AWS service meets these requirements?
- A . Amazon Comprehend
- B . Amazon Textract
- C . Amazon Kendra
- D . Amazon Personalize
Correct Answer: C
C
Explanation:
Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
Amazon Textract extracts structured text from scanned docs and PDFs. Amazon Personalize builds recommendation systems, not search engines.
Reference: AWS Documentation C Amazon Kendra
C
Explanation:
Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
Amazon Textract extracts structured text from scanned docs and PDFs. Amazon Personalize builds recommendation systems, not search engines.
Reference: AWS Documentation C Amazon Kendra
Question #60
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.
Correct Answer: A
A
Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
Example-Driven Prompts:
This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
Why Option A is Correct:
Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
Contextual Relevance: Aligns the model’s responses to the specific task of classifying sentiment.
Why Other Options are Incorrect:
B. Detailed explanation of sentiment analysis: Unnecessary for the model’s operation; it requires examples, not explanations.
C. New text passage without context: Provides no guidance or learning context for the model.
D. Unrelated task examples: Would confuse the model and lead to inaccurate results.
A
Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
Example-Driven Prompts:
This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
Why Option A is Correct:
Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
Contextual Relevance: Aligns the model’s responses to the specific task of classifying sentiment.
Why Other Options are Incorrect:
B. Detailed explanation of sentiment analysis: Unnecessary for the model’s operation; it requires examples, not explanations.
C. New text passage without context: Provides no guidance or learning context for the model.
D. Unrelated task examples: Would confuse the model and lead to inaccurate results.