AWS Certified AI Practitioner : Domain 3 - Applications of Foundation Models
120 Practice questions with explanations for AWS AIF-C01 Certification Exam across all domains
Having completed Domain 2 - Fundamentals of Generative AI, where we delved into the fundamentals of generative AI, it’s time to move forward to Domain 3: Applications of Foundation Models. This domain is the most extensive, comprising 28% of the exam content, and is vital for understanding how to apply foundation models in various real-world scenarios.
In this section, we’ll explore the practical applications of foundation models, such as how they can be adapted for specific tasks, fine-tuned for improved performance, and integrated into AI solutions. Mastery of this domain will not only help you excel in the exam but also enable you to leverage these powerful models effectively in your AI projects.
Below, you’ll find a set of carefully crafted practice questions focused on Domain 3. These questions will challenge your understanding and help you prepare for the real exam, ensuring you’re ready to handle the practical aspects of applying foundation models.
Domain 3: Applications of Foundation Models
1. What is Retrieval Augmented Generation (RAG)?
A) A technique for generating new data
B) A method of combining retrieved information with model generation
C) A type of model architecture
D) A data compression algorithm
Correct Answer: B
Explanation: RAG is a method of combining retrieved information with model generation, as mentioned in Task Statement 3.1.
2. Which AWS service is suitable for storing embeddings in a vector database?
A) Amazon S3
B) Amazon RDS
C) Amazon OpenSearch Service
D) Amazon EC2
Correct Answer: C
Explanation: Amazon OpenSearch Service is mentioned in Task Statement 3.1 as a service for storing embeddings in vector databases.
3. What is the primary purpose of adjusting the temperature parameter in inference?
A) To control the physical temperature of the server
B) To adjust the creativity or randomness of the model’s output
C) To increase the model’s processing speed
D) To reduce energy consumption
Correct Answer: B
Explanation: The temperature parameter affects the creativity or randomness of the model’s output, as implied in Task Statement 3.1 under inference parameters.
4. What is a chain-of-thought prompt?
A) A physical chain used in AI hardware
B) A prompt that encourages the model to show its reasoning process
C) A method of linking multiple AI models
D) A technique for encrypting prompts
Correct Answer: B
Explanation: Chain-of-thought is a prompt engineering technique that encourages the model to show its reasoning process, as mentioned in Task Statement 3.2.
5. Which of the following is NOT a typical method for fine-tuning a foundation model?
A) Instruction tuning
B) Transfer learning
C) Physical tuning
D) Continuous pre-training
Correct Answer: C
Explanation: Physical tuning is not a method for fine-tuning foundation models. The other options are mentioned in Task Statement 3.3.
6. What is the ROUGE metric used for in evaluating foundation models?
A) Measuring the redness of the model’s output
B) Evaluating the quality of generated summaries
C) Calculating the model’s energy efficiency
D) Determining the model’s processing speed
Correct Answer: B
Explanation: ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is used for evaluating the quality of generated summaries, as mentioned in Task Statement 3.4.
7. What is the primary purpose of using Agents for Amazon Bedrock?
A) To hire human agents for AI tasks
B) To handle multi-step tasks in AI applications
C) To physically maintain AI hardware
D) To reduce the cost of AI services
Correct Answer: B
Explanation: Agents for Amazon Bedrock are used to handle multi-step tasks in AI applications, as mentioned in Task Statement 3.1.
8. Which of the following is a key consideration when selecting a pre-trained model?
A) The model’s popularity on social media
B) The physical size of the server hosting the model
C) The model’s input/output length capabilities
D) The color scheme of the model’s documentation
Correct Answer: C
Explanation: The model’s input/output length capabilities are a key consideration when selecting a pre-trained model, as mentioned in Task Statement 3.1.
9. What is prompt hijacking in the context of prompt engineering?
A) A method of optimizing prompts
B) A technique for stealing prompts from competitors
C) An attack where the model is tricked into ignoring the intended prompt
D) A way to speed up prompt processing
Correct Answer: C
Explanation: Prompt hijacking is a risk where the model is tricked into ignoring the intended prompt, as implied in Task Statement 3.2 under potential risks of prompt engineering.
10. What is the primary goal of instruction tuning in foundation models?
A) To teach the model to follow specific instructions
B) To reduce the model’s size
C) To increase the model’s processing speed
D) To change the model’s programming language
Correct Answer: A
Explanation: Instruction tuning aims to teach the model to follow specific instructions, as mentioned in Task Statement 3.3.
11. What is BERTScore used for in evaluating foundation models?
A) Measuring the model’s energy efficiency
B) Evaluating the quality of generated text
C) Calculating the model’s processing speed
D) Determining the model’s market value
Correct Answer: B
Explanation: BERTScore is used for evaluating the quality of generated text, as mentioned in Task Statement 3.4.
12. What is a key benefit of using in-context learning for foundation model customization?
A) It requires no additional training data
B) It always produces perfect results
C) It reduces the model’s size
D) It eliminates the need for prompts
Correct Answer: A
Explanation: In-context learning allows for model customization without additional training data, as implied in Task Statement 3.1 under cost tradeoffs.
13. What is a potential risk of using zero-shot learning in prompt engineering?
A) The model may perform poorly on tasks it wasn’t explicitly trained for
B) The model will refuse to generate any output
C) The model will only work with numerical data
D) The model will consume excessive energy
Correct Answer: A
Explanation: Zero-shot learning may result in poor performance on tasks the model wasn’t explicitly trained for, as implied in Task Statement 3.2 under prompt engineering techniques.
14. What is the primary purpose of reinforcement learning from human feedback (RLHF) in foundation model training?
A) To reduce the model’s energy consumption
B) To improve the model’s performance based on human evaluations
C) To increase the model’s size
D) To translate the model into different languages
Correct Answer: B
Explanation: RLHF is used to improve the model’s performance based on human evaluations, as mentioned in Task Statement 3.3.
15. Which of the following is NOT a typical consideration when preparing data for fine-tuning a foundation model?
A) Data curation
B) Data size
C) Data labeling
D) Data color coding
Correct Answer: D
Explanation: Data color coding is not a typical consideration. The other options are mentioned in Task Statement 3.3 as considerations for preparing data for fine-tuning.
16. What is prompt templating in the context of prompt engineering?
A) A method of physically printing prompts
B) A technique for creating reusable prompt structures
C) A way to encrypt prompts
D) A process of translating prompts into different languages
Correct Answer: B
Explanation: Prompt templating is a technique for creating reusable prompt structures, as implied in Task Statement 3.2 under prompt engineering techniques.
17. What is the primary advantage of using few-shot learning in prompt engineering?
A) It requires no examples in the prompt
B) It allows the model to learn from a small number of examples
C) It always produces perfect results
D) It reduces the model’s energy consumption
Correct Answer: B
Explanation: Few-shot learning allows the model to learn from a small number of examples, as implied in Task Statement 3.2 under prompt engineering techniques.
18. Which AWS service is suitable for storing embeddings in a relational database?
A) Amazon DynamoDB
B) Amazon S3
C) Amazon Aurora
D) Amazon EC2
Correct Answer: C
Explanation: Amazon Aurora is mentioned in Task Statement 3.1 as a service for storing embeddings in databases.
19. What is a key consideration when evaluating whether a foundation model effectively meets business objectives?
A) The model’s popularity on social media
B) The physical size of the server hosting the model
C) The model’s impact on user engagement
D) The color scheme of the model’s user interface
Correct Answer: C
Explanation: The model’s impact on user engagement is a key consideration when evaluating business effectiveness, as mentioned in Task Statement 3.4.
20. What is the primary purpose of negative prompts in prompt engineering?
A) To make the model generate negative emotions
B) To tell the model what to avoid in its output
C) To reduce the model’s energy consumption
D) To decrease the model’s processing speed
Correct Answer: B
Explanation: Negative prompts are used to tell the model what to avoid in its output, as implied in Task Statement 3.2 under concepts of prompt engineering.
21. What is continuous pre-training in the context of foundation models?
A) A method of constantly retraining the model on new data
B) A technique for training models 24/7
C) A way to train models using continuous mathematics
D) A process of training models on a continuous physical surface
Correct Answer: A
Explanation: Continuous pre-training involves constantly retraining the model on new data, as mentioned in Task Statement 3.3.
22. What is prompt poisoning in the context of prompt engineering risks?
A) A method of optimizing prompts
B) A technique for improving prompt quality
C) An attack where malicious content is inserted into training data or prompts
D) A way to speed up prompt processing
Correct Answer: C
Explanation: Prompt poisoning is an attack where malicious content is inserted into training data or prompts, as implied in Task Statement 3.2 under potential risks of prompt engineering.
23. What is the BLEU score used for in evaluating foundation models?
A) Measuring the model’s energy efficiency
B) Evaluating the quality of machine translations
C) Calculating the model’s processing speed
D) Determining the model’s market value
Correct Answer: B
Explanation: BLEU (Bilingual Evaluation Understudy) is used for evaluating the quality of machine translations, as mentioned in Task Statement 3.4.
24. What is a key benefit of using transfer learning for foundation model customization?
A) It requires no additional training
B) It allows the model to leverage knowledge from one domain to another
C) It always produces perfect results
D) It reduces the model’s size to zero
Correct Answer: B
Explanation: Transfer learning allows the model to leverage knowledge from one domain to another, as mentioned in Task Statement 3.3.
25. What is the primary purpose of model latent space in the context of prompt engineering?
A) To physically store the model
B) To represent the model’s internal understanding and knowledge
C) To increase the model’s processing speed
D) To reduce the model’s energy consumption
Correct Answer: B
Explanation: The model latent space represents the model’s internal understanding and knowledge, as implied in Task Statement 3.2 under concepts of prompt engineering.
Prepare for Domain 4:
As you finish with Domain 3, get ready for our next post, where we’ll cover Guidelines for Responsible AI. This domain is essential for understanding the ethical considerations and guidelines that ensure AI is developed and deployed responsibly. You can find the next post here: [AWS Certified AI Practitioner: Domain 4 - Guidelines for Responsible AI].
Let’s continue this journey together as we move closer to your AWS Certified AI Practitioner certification! Keep up the hard work — you’re doing great!
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