Practice Free AIF-C01 Exam Online Questions
Question #31
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
- A . Code for model training
- B . Partial dependence plots (PDPs)
- C . Sample data for training
- D . Model convergence tables
Correct Answer: B
B
Explanation:
Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency and explainability of the model’s behavior to stakeholders by illustrating how different input variables impact the model’s predictions.
Option B (Correct): "Partial dependence plots (PDPs)": This is the correct answer because PDPs help to interpret how the model’s predictions change with varying values of input features, providing stakeholders with a clearer understanding of the model’s decision-making process.
Option A: "Code for model training" is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.
Option C: "Sample data for training" is incorrect as sample data alone does not explain how the model works or its decision-making process.
Option D: "Model convergence tables" is incorrect. While convergence tables can show the training process, they do not provide insights into how input features affect the model’s predictions. AWS AI Practitioner
Reference: Explainability in AWS Machine Learning: AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model’s predictions.
B
Explanation:
Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency and explainability of the model’s behavior to stakeholders by illustrating how different input variables impact the model’s predictions.
Option B (Correct): "Partial dependence plots (PDPs)": This is the correct answer because PDPs help to interpret how the model’s predictions change with varying values of input features, providing stakeholders with a clearer understanding of the model’s decision-making process.
Option A: "Code for model training" is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.
Option C: "Sample data for training" is incorrect as sample data alone does not explain how the model works or its decision-making process.
Option D: "Model convergence tables" is incorrect. While convergence tables can show the training process, they do not provide insights into how input features affect the model’s predictions. AWS AI Practitioner
Reference: Explainability in AWS Machine Learning: AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model’s predictions.
Question #32
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model’s performance decreased significantly.
What should the company do to mitigate this problem?
- A . Reduce the volume of data that is used in training.
- B . Add hyperparameters to the model.
- C . Increase the volume of data that is used in training.
- D . Increase the model training time.
Correct Answer: C
C
Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data. AWS AI Practitioner
Reference: Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model’s generalization capability and reduce the risk of overfitting.
C
Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data. AWS AI Practitioner
Reference: Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model’s generalization capability and reduce the risk of overfitting.
Question #33
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.
Which solution will meet these requirements?
- A . Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.
- B . Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.
- C . Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.
- D . Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.
Correct Answer: A
Question #34
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
- A . Create documents with the relevant information. Store the documents in Amazon S3.
- B . Use AWS A] Service Cards for transparency and understanding models.
- C . Create Amazon SageMaker Model Cards with Intended uses and training and inference details.
- D . Create model training scripts. Commit the model training scripts to a Git repository.
Correct Answer: C
C
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations."
(Source: Amazon SageMaker Developer Guide, SageMaker Model Cards)
Detailed
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models. AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details. This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team’s requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository. Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
Reference: Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing
AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
C
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations."
(Source: Amazon SageMaker Developer Guide, SageMaker Model Cards)
Detailed
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models. AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details. This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team’s requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository. Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
Reference: Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing
AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
Question #35
Which metric measures the runtime efficiency of operating AI models?
- A . Customer satisfaction score (CSAT)
- B . Training time for each epoch
- C . Average response time
- D . Number of training instances
Correct Answer: C
C
Explanation:
The average response time is the correct metric for measuring the runtime efficiency of operating AI models.
Average Response Time:
Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.
A lower average response time indicates a more efficient model that can handle queries quickly.
Why Option C is Correct:
Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.
Performance Indicator: Helps identify potential bottlenecks and optimize model performance.
Why Other Options are Incorrect:
C
Explanation:
The average response time is the correct metric for measuring the runtime efficiency of operating AI models.
Average Response Time:
Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.
A lower average response time indicates a more efficient model that can handle queries quickly.
Why Option C is Correct:
Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.
Performance Indicator: Helps identify potential bottlenecks and optimize model performance.
Why Other Options are Incorrect:
Question #36
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company’s private network.
Which solution will meet these requirements?
- A . Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.
- B . Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
- C . Use AWS PrivateLink to connect the VPC and Amazon Bedrock.
- D . Use AWS Key Management Service (AWS KMS) keys to encrypt the data.
Correct Answer: C
C
Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company’s network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking)
Detailed
Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role. While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer.
This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy. IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock. This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data. While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference: AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/)
AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services
C
Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company’s network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking)
Detailed
Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role. While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer.
This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy. IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock. This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data. While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference: AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/)
AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services
Question #37
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?
- A . Amazon Rekognition
- B . Amazon Bedrock playgrounds
- C . Guardrails for Amazon Bedrock
- D . Agents for Amazon Bedrock
Correct Answer: C
C
Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI
applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children. AWS AI Practitioner
Reference: Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.
C
Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI
applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children. AWS AI Practitioner
Reference: Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.
Question #38
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company’s website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?
- A . Data leakage
- B . Prompt injection
- C . Large language model (LLM) hallucinations
- D . Concept drift
Correct Answer: B
B
Explanation:
The ecommerce company’s chatbot, powered by AI, automates customer order submissions and is accessible 24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot’s behavior, such as bypassing safeguards or accessing unauthorized information. This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model’s behavior, potentially leading to unauthorized actions or harmful outputs.
Implementing guardrails and input validation can mitigate this risk."
(Source: AWS Bedrock User Guide, Security Best Practices)
Detailed
Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot’s operation.
Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
Reference: AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)
B
Explanation:
The ecommerce company’s chatbot, powered by AI, automates customer order submissions and is accessible 24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot’s behavior, such as bypassing safeguards or accessing unauthorized information. This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model’s behavior, potentially leading to unauthorized actions or harmful outputs.
Implementing guardrails and input validation can mitigate this risk."
(Source: AWS Bedrock User Guide, Security Best Practices)
Detailed
Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot’s operation.
Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
Reference: AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)
Question #39
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 #40
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
- A . Multi-modal embedding model
- B . Text embedding model
- C . Multi-modal generation model
- D . Image generation model
Correct Answer: A
A
Explanation:
A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images. Multi-Modal Embedding Model:
Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.
Suitable for applications where queries or content involve multiple data modalities.
Why Option A is Correct:
Handles Multiple Modalities: Supports both text and image data, aligning with the application’s requirement.
Improves Search Relevance: Allows for more accurate and relevant search results across different types of input data.
Why Other Options are Incorrect:
B. Text embedding model: Only handles text data, not images.
C. Multi-modal generation model: Focuses on generating outputs rather than embedding for search tasks.
D. Image generation model: Only handles image data, not suitable for text queries.
A
Explanation:
A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images. Multi-Modal Embedding Model:
Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.
Suitable for applications where queries or content involve multiple data modalities.
Why Option A is Correct:
Handles Multiple Modalities: Supports both text and image data, aligning with the application’s requirement.
Improves Search Relevance: Allows for more accurate and relevant search results across different types of input data.
Why Other Options are Incorrect:
B. Text embedding model: Only handles text data, not images.
C. Multi-modal generation model: Focuses on generating outputs rather than embedding for search tasks.
D. Image generation model: Only handles image data, not suitable for text queries.