Generative AI Leader Fundamentals of Generative AI Practice Test 8 — 15 Questions

Question 1

An organization wants to use a generative model to automatically generate legal contracts. The model must produce clauses that are not only grammatically correct but also legally enforceable and consistent with current jurisdiction laws. Which combination of techniques best ensures legal compliance?

Accepted Answer

Fine-tune a model on a diverse set of enforceable contracts and incorporate an external compliance verifier that uses rule-based checks.. Option D is correct because fine-tuning on curated legal documents teaches domain-specific language and enforceability, while a verifier (an external logic/rule system) checks compliance with laws. Option A is incorrect because prompt engineering is unreliable for precise legal reasoning. Option B is incorrect because small model likely lacks legal knowledge. Option C is incorrect because RAG retrieves but does not verify enforceability.

Answer

Fine-tune a small model exclusively on legal contracts from a single jurisdiction and use it for generation.

Answer

Implement retrieval-augmented generation (RAG) with a vector database of all relevant laws.

Answer

Use a large instruction-tuned model with carefully engineered prompts describing jurisdiction details.

Question 2

Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)

Accepted Answer

Generative models can create new data samples, while discriminative models only assign labels to existing data.. Option A is correct because generative AI models learn the underlying distribution of the data, enabling them to generate new, realistic samples (e.g., images, text) from the learned distribution. In contrast, discriminative models learn decision boundaries to classify or label existing data without the ability to create new data instances. This fundamental difference in capability—creation versus discrimination—is a core distinction between the two paradigms.

Answer

Generative models require less training data than discriminative models.

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Generative models cannot be used for supervised learning tasks like classification.

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Discriminative models always outperform generative models on tasks like image classification.

Question 3

Which THREE of the following are common techniques to reduce harmful biases in generative AI models? (Choose three.)

Accepted Answer

Use reinforcement learning from human feedback (RLHF) with a reward model that penalizes biased or unfair outputs.. A is correct because RLHF uses a reward model trained on human preferences to score model outputs, and explicitly penalizing biased or unfair outputs during fine-tuning directly reduces harmful biases. This technique aligns the model's behavior with human values by optimizing against a learned reward signal that captures bias-related concerns.

Answer

Decrease the model's temperature parameter to make outputs more deterministic.

Answer

Conduct a legal review of all generated outputs before release.

Question 4

Which THREE of the following are key considerations when deploying a generative AI model in a production environment with strict latency requirements? (Choose three.)

Accepted Answer

Implement speculative decoding to generate candidate tokens with a smaller draft model and verify with the large model.. Option B is correct because speculative decoding uses a smaller, faster draft model to generate candidate tokens, which are then verified by the large model in parallel. This reduces the number of sequential autoregressive steps, significantly lowering latency while maintaining output quality.

Answer

Deploy the largest model variant available to ensure highest quality.

Answer

Increase the inference batch size to maximize GPU utilization.

Question 5

You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

Accepted Answer

Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.. Option D is correct because prompt engineering is the most effective and cost-efficient way to improve LLM output without retraining or changing the model. By explicitly instructing the model to include medication dosages and allergies, and by structuring the input with clear headings, you guide the model's attention to critical sections, directly addressing the missing details. This approach leverages the LLM's existing capabilities and requires no changes to the hosted API or additional infrastructure.

Answer

Increase the maximum output token limit to force the model to include more details.

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Replace the LLM with a simpler extractive summarization model that selects sentences from the original document.

Answer

Implement a retrieval-augmented generation (RAG) system that pulls supplementary data from external drug databases.

Question 6

You are the lead AI engineer at a financial services firm. You have fine-tuned a large language model on historical trade reports to generate daily market summaries. The model is deployed on Google Cloud's Vertex AI using a custom container. A few weeks after deployment, the operations team notices that inference latency has increased by 300%, causing timeouts. You investigate and find that the model's memory consumption has grown unexpectedly, and the GPUs are idling due to high data transfer wait times. The model architecture and code have not changed. Which action is most likely to resolve the latency issue?

Accepted Answer

Periodically clear the key-value cache between inference requests and implement cache truncation for long sequences.. The latency spike is caused by the key-value (KV) cache growing unboundedly across inference requests, leading to excessive memory consumption and data transfer wait times. Periodically clearing the KV cache between requests and truncating it for long sequences directly addresses the root cause by freeing GPU memory and reducing I/O bottlenecks, without requiring hardware upgrades or recompilation.

Answer

Upgrade to a more powerful GPU instance (e.g., A100 to H100) to handle the increased memory footprint.

Answer

Enable preemptible VM instances to reduce cost and redeploy the model on a faster network.

Answer

Recompile the model using XLA with optimizations for dynamic shapes.

Question 7

You are a generative AI architect at a social media company. You are tasked with building a content moderation system that uses a generative model to flag toxic comments. The system must have very low false positive rates (i.e., not flag harmless comments) to avoid user backlash, but it must catch nearly all toxic comments. You have a large dataset of labeled toxic and non-toxic comments. You plan to use a pre-trained LLM and fine-tune it for classification. During experimentation, you notice that the model's recall for toxic comments is high (95%) but its precision is low (60%), leading to many false positives. You need to improve precision without substantially reducing recall. Which approach should you try first?

Accepted Answer

Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic).. Option D is correct because threshold tuning is a straightforward, post-hoc method to trade recall for precision by raising the decision threshold. Option A is incorrect because adding more toxic samples might increase recall but not necessarily precision. Option B is incorrect because a smaller model might have less capacity to distinguish nuance, worsening precision. Option C is incorrect because class weighting can improve recall but may hurt precision.

Answer

Gather additional toxic comments from similar platforms to augment the training data.

Answer

Apply a higher weight to the toxic class in the loss function during fine-tuning.

Answer

Use a smaller pre-trained model that is inherently less sensitive to subtle toxic language.

Question 8

You are the AI lead at an e-commerce company that uses a generative model to write product descriptions from images and key attributes. The model is a multimodal transformer that encodes both image and text (attributes) and decodes a description. Recently, your team deployed a new version of the image encoder that uses a more powerful backbone (ViT-L instead of ViT-B). After deployment, the generated descriptions became longer but often include irrelevant visual details (e.g., background objects) and occasionally misrepresent the product's main features. The model was fine-tuned on the same dataset as before. The descriptions from the old model were concise and focused. What is the most likely cause of the degradation and the best fix?

Accepted Answer

The powerful encoder introduces overfitting to the training images; continue fine-tuning with additional loss terms that penalize description of irrelevant details (e.g., using attention regularization).. Option D is correct because the more powerful ViT-L encoder captures richer, high-resolution features, including background details, which the decoder then amplifies into longer, less focused descriptions. This is a form of overfitting to irrelevant visual patterns in the training images, not to the labels. Adding attention regularization (e.g., penalizing attention weights on non-salient regions) forces the model to focus on product-relevant features, restoring conciseness and accuracy without reverting to the weaker encoder.

Answer

The decoder is now too small relative to the encoder; reduce the encoder's hidden size or increase the decoder's capacity.

Answer

The new encoder produces less discriminative features; replace it with an older version.

Answer

Lower the decoder's temperature to reduce diversity and hallucination.

Question 9

A company is building a conversational AI using the Gemini API on Vertex AI. They want to reduce the chance of generating toxic content while still allowing creative and engaging responses for their gaming community. Which TWO safety settings should they adjust in the safety_settings parameter?

Accepted Answer

Enable the 'harm_category' filter for 'DANGEROUS_CONTENT' with threshold BLOCK_ONLY_HIGH.. Option C is correct because setting the 'DANGEROUS_CONTENT' category to BLOCK_ONLY_HIGH allows the model to generate creative and engaging responses for a gaming community while still blocking the most severe dangerous content. This balances safety with creative freedom, as the gaming context may involve simulated conflict or action that is not genuinely harmful.

Answer

Set the threshold for 'SEXUALLY_EXPLICIT' category to BLOCK_NONE.

Answer

Set the threshold for 'SEXUALLY_EXPLICIT' category to BLOCK_LOW_AND_ABOVE.

Answer

Set the threshold for 'HARASSMENT' category to BLOCK_LOW_AND_ABOVE.

Question 10

A startup is developing a customer support chatbot using Vertex AI PaLM 2 API. They notice that the model sometimes generates plausible-sounding but factually incorrect information about company policies. The chatbot currently uses no external data. To reduce these hallucinations without retraining the model, the team needs a solution that can be implemented quickly and maintains low latency. They have access to the company's internal policy database stored in Cloud SQL. Which approach should they take?

Accepted Answer

Implement grounding by connecting the model to the company's policy database using Vertex AI Grounding.. Option B is correct because Vertex AI Grounding connects the PaLM 2 model to the company's policy database in Cloud SQL, allowing the model to retrieve and cite factual information in real time. This approach reduces hallucinations without retraining, meets the low-latency requirement, and leverages existing internal data. Grounding works by augmenting the prompt with retrieved context from the grounding source, ensuring responses are factually grounded.

Answer

Fine-tune the PaLM 2 model on a dataset of company policy documents.

Answer

Reduce the temperature parameter to 0 and increase top_k to 50.

Answer

Use prompt engineering to instruct the model to only answer from its internal knowledge.

Question 11

A marketing team wants to generate product descriptions using a text generation model on Vertex AI. They need consistent output style across all descriptions, including tone and length. They have a small set of 10 high-quality example descriptions that capture the desired style. The team has limited ML expertise and wants a quick solution that does not require model retraining. Which approach should they use?

Accepted Answer

Use few-shot prompting with the examples in the prompt.. Few-shot prompting is the correct approach because it allows the team to inject the desired style, tone, and length directly into the prompt using the 10 high-quality examples, without any model retraining. This technique leverages the in-context learning capability of large language models on Vertex AI, enabling consistent output from a small set of demonstrations. It is ideal for teams with limited ML expertise as it requires only prompt engineering, not fine-tuning or infrastructure changes.

Answer

Use a pre-built template with no model input.

Answer

Fine-tune the model on a large external dataset of product descriptions.

Answer

Set the temperature to 0.9 to maximize creativity.

Question 12

A healthcare company is building a clinical decision support system using Gemini 1.5 Pro on Vertex AI. They need responses that are highly accurate and comply with medical regulations, including traceability to source documents. They have a large corpus of curated medical guidelines stored in PDFs in Cloud Storage. Their team has experience with both fine-tuning and prompt engineering. Which approach best ensures regulatory compliance and accuracy?

Accepted Answer

Use a combination of grounding to the medical guidelines and prompt engineering with system instructions specifying compliance requirements.. Option D is correct because combining grounding (which ties answers to the actual guidelines) with prompt engineering (which enforces compliance requirements) provides traceability and accuracy. Option A (fine-tuning only) risks the model memorizing rather than citing sources, and updates require retraining. Option B (grounding only) may still allow the model to generate ungrounded responses if not properly constrained. Option C (prompt engineering only) relies on the model's pre-trained knowledge, which is less reliable.

Answer

Use prompt engineering with system instructions and few-shot examples, but no grounding.

Answer

Use grounding to the medical guidelines but rely on prompt engineering only for compliance instructions.

Answer

Fine-tune the model on the medical guidelines corpus to internalize the knowledge.

Question 13

A company is deploying a Gemini 1.0 Ultra model for a code generation assistant. They have set up Vertex AI Model Evaluation with a custom evaluation dataset to measure pass@1 accuracy. The initial evaluation shows 65% pass@1. They want to improve to 80% without collecting more training data. They have already attempted basic prompt engineering (e.g., 'write correct code') with limited improvement. Which approach is most likely to achieve the desired improvement?

Accepted Answer

Use chain-of-thought prompting with few-shot examples of correct code generation.. Chain-of-thought prompting with few-shot examples is the most effective approach because it guides the model through step-by-step reasoning, which is critical for complex code generation tasks. This technique leverages the model's in-context learning ability to improve accuracy without additional training data, directly addressing the need to boost pass@1 from 65% to 80%.

Answer

Reduce the temperature to 0 and set top_p to 1.

Answer

Increase the number of output tokens and enable beam search with width 4.

Answer

Apply reinforcement learning from human feedback (RLHF) using a reward model trained on the existing evaluation dataset.

Question 14

A large enterprise runs a production application that uses the Gemini API on Vertex AI for real-time content moderation. They are experiencing occasional 429 (Too Many Requests) errors during peak hours. Their current quota is 1000 requests per minute (RPM) and they are hitting around 950 RPM on average, with spikes up to 1050. They have already implemented exponential backoff and retry logic. They need to reduce the error rate without reducing the quality of moderation. Which additional measure should they take?

Accepted Answer

Implement a local caching layer for common moderation queries.. Option C is correct because implementing a local caching layer for common moderation queries reduces the number of identical requests sent to the Gemini API, directly lowering the effective RPM without compromising moderation quality. Since the enterprise is already using exponential backoff and retry logic, caching addresses the root cause of hitting quota limits by eliminating redundant API calls, which is a standard pattern for rate-limit mitigation in production AI workloads.

Answer

Deploy the model on a dedicated Vertex AI endpoint with autoscaling.

Answer

Switch to a lower-tier model like Gemini 1.0 Pro to reduce quota consumption.

Answer

Request a quota increase from Google Cloud support.

Question 15

A financial services company is building a customer service agent using Vertex AI Agent Builder. They want the agent to only answer questions based on their approved policy documents, which are stored in Cloud Storage. They also need to ensure that the agent never reveals internal employee names or account numbers. They have set up grounding with the documents but find that the agent sometimes ignores the grounding and generates responses using the model's internal knowledge. What should they do to strictly constrain the agent to only use the provided documents?

Accepted Answer

Use the 'vertex-ai-agent-builder' with strict grounding mode and disable fallback to model knowledge.. Option C is correct because Agent Builder provides a 'strict grounding' mode that prevents the model from falling back to internal knowledge, ensuring responses rely solely on the grounded documents. Option A (temperature adjustment) does not force grounding. Option B (system instruction) may be overridden. Option D (fine-tuning) does not fully block internal knowledge and requires effort.

Answer

Add a system instruction that says 'Only answer from the provided documents.'

Answer

Set the model's temperature to 0 and top_p to 0.1.

Answer

Fine-tune the model on the policy documents to limit its knowledge.