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Question 210
An executive asks a model for a very recent financial filing that was published after training. Which solution improves supportability?
- Multimodal understanding
- Grounded generation
- Tool-assisted actions
- Long-context processing
Explanation
Correct Answer
B:Fresh grounding can retrieve up-to-date source material. Grounding supplies external evidence and attribution to reduce unsupported model assertions.
Incorrect Answers
A:Multimodal understanding interprets mixed inputs such as images, written text and audio. This does not directly meet the case requirement to reference newly published evidence in an answer.
C:Tool use connects model reasoning to controlled functions and external systems. This does not directly meet the case requirement to reference newly published evidence in an answer.
D:Long-context models can consider more source material in a single request within token limits. This does not directly meet the case requirement to reference newly published evidence in an answer.
Question 211
A research organization insists on inspecting and hosting model weights directly. Which deployment characteristic is essential?
- Open-weight deployment
- Managed model API
- Model license review
- Fine-tuning
Explanation
Correct Answer
A:Open-weight access enables customer-controlled model deployment. Open-weight models allow authorized users to access model weights under applicable licensing terms.
Incorrect Answers
B:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to access model parameters for self-hosting.
C:License review checks permitted redistribution, usage and other conditions before deployment. This does not directly meet the case requirement to access model parameters for self-hosting.
D:Fine-tuning updates model weights with task-specific examples to change model behavior. This does not directly meet the case requirement to access model parameters for self-hosting.
Question 212
A small company wants to use a capable model immediately without managing GPU servers. Which deployment option is appealing?
- On-premises hosting
- Retrieval augmentation
- Managed model API
- Model license review
Explanation
Correct Answer
C:A managed endpoint transfers serving operations to the provider. A managed API can provide model inference without the customer operating serving infrastructure.
Incorrect Answers
A:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to consume inference without operating infrastructure.
B:Retrieval augmentation adds external facts at query time without changing base model weights. This does not directly meet the case requirement to consume inference without operating infrastructure.
D:License review checks permitted redistribution, usage and other conditions before deployment. This does not directly meet the case requirement to consume inference without operating infrastructure.
Question 213
A hospital requires model execution on isolated hardware it controls. Which operational approach is relevant?
- On-premises hosting
- Fine-tuning
- Managed model API
- Retrieval augmentation
Explanation
Correct Answer
A:Local hosting supplies control but creates deployment responsibilities. On-premises hosting keeps serving within customer-managed hardware but requires local operations.
Incorrect Answers
B:Fine-tuning updates model weights with task-specific examples to change model behavior. This does not directly meet the case requirement to run inference on customer-managed equipment.
C:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to run inference on customer-managed equipment.
D:Retrieval augmentation adds external facts at query time without changing base model weights. This does not directly meet the case requirement to run inference on customer-managed equipment.
Question 214
A bilingual support project has verified task examples and needs adapted language behavior over many requests. Which technique changes learned weights?
- Model license review
- Fine-tuning
- On-premises hosting
- Managed model API
Explanation
Correct Answer
B:Fine-tuning can adapt a pretrained model using curated data. Fine-tuning updates model weights with task-specific examples to change model behavior.
Incorrect Answers
A:License review checks permitted redistribution, usage and other conditions before deployment. This does not directly meet the case requirement to update model behavior using task examples.
C:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to update model behavior using task examples.
D:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to update model behavior using task examples.
Question 215
A legal assistant's handbook changes daily but the base model need not change. Which technique keeps facts fresh?
- On-premises hosting
- Managed model API
- Retrieval augmentation
- Open-weight deployment
Explanation
Correct Answer
C:Retrieval accesses new approved documents during inference. Retrieval augmentation adds external facts at query time without changing base model weights.
Incorrect Answers
A:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to inject current policy evidence dynamically.
B:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to inject current policy evidence dynamically.
D:Open-weight models allow authorized users to access model weights under applicable licensing terms. This does not directly meet the case requirement to inject current policy evidence dynamically.
Question 216
A company wants to redistribute a customized open-weight model in its product. What must it check first?
- Model license review
- Managed model API
- Retrieval augmentation
- Open-weight deployment
Explanation
Correct Answer
A:Availability of weights does not remove license conditions. License review checks permitted redistribution, usage and other conditions before deployment.
Incorrect Answers
B:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to verify redistribution and commercial-use terms.
C:Retrieval augmentation adds external facts at query time without changing base model weights. This does not directly meet the case requirement to verify redistribution and commercial-use terms.
D:Open-weight models allow authorized users to access model weights under applicable licensing terms. This does not directly meet the case requirement to verify redistribution and commercial-use terms.
Question 217
An engineering team prefers to run a Google open-weight language model inside its own cluster. Which model characteristic matters?
- Open-weight deployment
- Model license review
- On-premises hosting
- Managed model API
Explanation
Correct Answer
A:Weights must be available for authorized deployment. Open-weight models allow authorized users to access model weights under applicable licensing terms.
Incorrect Answers
B:License review checks permitted redistribution, usage and other conditions before deployment. This does not directly meet the case requirement to host an open-weight model independently.
C:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to host an open-weight model independently.
D:A managed API can provide model inference without the customer operating serving infrastructure. This does not directly meet the case requirement to host an open-weight model independently.
Question 218
A startup cannot afford to maintain model serving and wants uptime managed as a service. Which delivery model fits?
- Managed model API
- Open-weight deployment
- On-premises hosting
- Retrieval augmentation
Explanation
Correct Answer
A:Managed inference reduces operational burden. A managed API can provide model inference without the customer operating serving infrastructure.
Incorrect Answers
B:Open-weight models allow authorized users to access model weights under applicable licensing terms. This does not directly meet the case requirement to use hosted model serving.
C:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to use hosted model serving.
D:Retrieval augmentation adds external facts at query time without changing base model weights. This does not directly meet the case requirement to use hosted model serving.
Question 219
A Gemma-based assistant answers with outdated travel policy despite successful fine-tuning last year. What is the most direct freshness mechanism?
- Retrieval augmentation
- Open-weight deployment
- Model license review
- Fine-tuning
Explanation
Correct Answer
A:Retrieval provides updated instructions without retraining for every change. Retrieval augmentation adds external facts at query time without changing base model weights.
Incorrect Answers
B:Open-weight models allow authorized users to access model weights under applicable licensing terms. This does not directly meet the case requirement to access current documents at generation time.
C:License review checks permitted redistribution, usage and other conditions before deployment. This does not directly meet the case requirement to access current documents at generation time.
D:Fine-tuning updates model weights with task-specific examples to change model behavior. This does not directly meet the case requirement to access current documents at generation time.
Question 220
A product group assumes open-weight automatically means unrestricted licensing. Which activity corrects the assumption?
- Open-weight deployment
- Retrieval augmentation
- On-premises hosting
- Model license review
Explanation
Correct Answer
D:Permission to use weights remains governed by applicable terms. License review checks permitted redistribution, usage and other conditions before deployment.
Incorrect Answers
A:Open-weight models allow authorized users to access model weights under applicable licensing terms. This does not directly meet the case requirement to review the actual distribution license.
B:Retrieval augmentation adds external facts at query time without changing base model weights. This does not directly meet the case requirement to review the actual distribution license.
C:On-premises hosting keeps serving within customer-managed hardware but requires local operations. This does not directly meet the case requirement to review the actual distribution license.
Generative AI Leader Practice Tests