Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 2 — 15 Questions

Question 1

A developer is using the Gemini API to generate code snippets. They notice the outputs often contain deprecated API calls. Which parameter adjustment or prompt strategy would most effectively encourage the model to use current APIs?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Add a system instruction specifying 'Use the most recent API version and avoid deprecated functions.'. Adding a system instruction that explicitly directs the model to 'Use the most recent API version and avoid deprecated functions' directly influences the model's behavior at the prompt level. The Gemini API supports system instructions that act as persistent, high-level guidance, steering the model toward preferred output patterns—in this case, avoiding deprecated API calls. This is the most effective and direct method to enforce current API usage without altering sampling parameters or relying on limited examples.

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Set top-p to 0.5 to reduce output diversity

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Provide one few-shot example of a correct API call

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Set temperature to 1.5 to increase creativity

Question 2

An e-commerce company fine-tunes a model on customer reviews to generate product feedback summaries. They want to ensure the model does not reproduce toxic language from the training data. Besides filtering the training data, which additional technique is most effective at inference time?

Accepted Answer

Pass the model output through a toxicity detection model and conditionally regenerate or block. It directly addresses the safety requirement at inference time by introducing a secondary guardrail. A toxicity detection model (e.g., a classifier trained on the Jigsaw Toxic Comment dataset) can score the generated output in real time; if the score exceeds a threshold, the system can either block the response or trigger a regeneration with adjusted parameters. This is the only technique that actively filters for toxic language after generation, rather than merely reducing output variance or exploring alternative sequences.

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Set temperature to 0.0 to reduce variance

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Set top-k to 10 to limit token choices

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Use beam search with a high beam width

Question 3

A data scientist is using Vertex AI generative AI studio to create a chatbot. The chatbot gives inconsistent answers to similar questions. Which parameter should they adjust to make responses more consistent?

Accepted Answer

Decrease temperature to 0.2. Decreasing the temperature to 0.2 reduces the randomness of the model's token sampling, making the output more deterministic and consistent. Temperature controls the probability distribution over tokens; lower values make the model more likely to choose the highest-probability token, reducing variability in responses to similar questions.

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Increase top-p to 0.9

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Increase presence penalty to 0.5

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Decrease frequency penalty to 0.0

Question 4

A developer uses the Gemini API to summarize long articles. The summaries often miss key points from the end of the article. Which technique specifically addresses this length-based loss of information?

Accepted Answer

Break the article into sections and ask the model to summarize each section, then combine. The Gemini API, like many LLMs, has a limited context window and exhibits a 'lost-in-the-middle' effect where information at the beginning and end of long inputs is retained better, but the middle and far end can be dropped. By breaking the article into sections, summarizing each independently, and then combining those summaries, you ensure that key points from the end are captured in their own focused summary, bypassing the length-based information loss. This technique is a form of 'chunking' and 'recursive summarization' that directly addresses the model's tendency to lose context over long sequences.

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Increase the max output tokens to 2048

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Truncate the article to the first 2000 tokens

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Use a different model with a larger context window

Question 5

A healthcare startup fine-tunes a model to generate patient education materials. They want to ensure the model never gives medical advice, only information. They add a safety instruction, but the model sometimes still gives advice. What advanced technique should they apply?

Accepted Answer

Apply RLHF with a reward model that penalizes outputs containing medical advice. RLHF (Reinforcement Learning from Human Feedback) directly addresses the model's behavior by training a reward model that penalizes outputs containing medical advice. This aligns the model's generation with the safety instruction at a fundamental level, rather than relying on brittle post-hoc filters or static embeddings that can be easily circumvented by novel phrasings.

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Hard-code a list of prohibited phrases in a post-processing script

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Add a secondary classifier to rewrite any detected advice into general information

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Use semantic similarity to a 'medical advice' embedding and reject if close

Question 6

A company is prompt engineering a model for customer support. They want to reduce hallucination (false information) in responses. Which TWO techniques are most effective? (Choose two.)

Accepted Answer

Implement RAG to retrieve relevant documents for context. Retrieval-Augmented Generation (RAG) grounds the model's output in external, verifiable documents retrieved from a knowledge base. By providing relevant context at inference time, RAG significantly reduces the likelihood of the model fabricating information, as it can reference and paraphrase from the retrieved sources rather than relying solely on its parametric memory.

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Provide 3 few-shot examples of conversations

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Reduce max output tokens to 150

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Increase temperature to 1.2

Question 7

A team is fine-tuning a model for a legal document summarization task. They need to ensure high accuracy and avoid hallucinations. Which TWO approaches should they combine? (Choose two.)

Accepted Answer

Use Retrieval-Augmented Generation to retrieve relevant legal texts. Retrieval-Augmented Generation (RAG) is correct because it grounds the model's output in retrieved, authoritative legal texts, directly reducing hallucination by providing factual context during generation. This is critical for legal summarization where accuracy is paramount, as RAG ensures the model references specific statutes or case law rather than relying solely on its parametric memory.

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Increase temperature to 1.5 during inference

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Implement early stopping during fine-tuning

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Use character-level tokenization to improve spelling

Question 8

A company wants to build a customer support chatbot that answers based on internal documentation. They use Vertex AI Search and want to ensure the model only uses retrieved documents. What should they do?

Accepted Answer

Enable grounding with Vertex AI Search. Grounding with Vertex AI Search ensures the model's responses are strictly based on the retrieved documents from the internal documentation, preventing hallucination or reliance on pre-trained knowledge. Grounding works by providing the model with a search result context that it must use as the sole source for generating answers, effectively constraining the output to the provided documents.

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Fine-tune the model on the documentation

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Increase max output tokens

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Set temperature to 0.0

Question 9

A company uses Vertex AI PaLM for code generation. The code often contains security vulnerabilities. Which improvement should be applied?

Accepted Answer

Include a security-focused system instruction. Including a security-focused system instruction directly guides the model to prioritize secure coding practices, such as input validation and proper error handling, reducing vulnerabilities. This leverages prompt engineering to shape model behavior without altering parameters like temperature or top_k, which control randomness, not security awareness.

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Set top_k to 1

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Use Codey model instead

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Increase temperature to 0.8

Question 10

After fine-tuning a model on customer support data, the model starts using profanity. What is the most effective mitigation?

Accepted Answer

Enable a safety attribute filter. Enabling a safety attribute filter is the most effective mitigation because it acts as a post-processing guardrail that blocks profanity at inference time, regardless of the model's training data. This is a standard practice in production LLM deployments, where safety filters (e.g., using keyword matching or classifier models) intercept and redact harmful outputs before they reach the user, providing immediate and reliable control without requiring retraining.

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Add profanity to training data as negative examples

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Reduce learning rate and retrain

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Increase temperature to reduce confidence

Question 11

Which technique allows a model to incorporate real-time data from external APIs?

Accepted Answer

RAG with tool calling. RAG with tool calling is correct because it enables a generative AI model to query external APIs in real-time, retrieve up-to-date information, and incorporate that data into its response. This technique combines retrieval-augmented generation (RAG) with function calling, where the model outputs a structured request (e.g., a JSON object) to invoke an API, receive the result, and then generate a context-aware answer. Unlike static methods, this allows dynamic data integration without retraining.

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Prompt engineering

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Fine-tuning

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Model pruning

Question 12

A model generates biased output. Which technique is least effective?

Accepted Answer

Set frequency penalty to 1.0. Setting the frequency penalty to 1.0 is least effective for reducing biased output because frequency penalties reduce repetition of tokens based on their frequency in the generated text, not their association with protected attributes or fairness. This parameter controls lexical diversity, not demographic parity or representational harm, so it does not address the root cause of bias in the model's training data or inference logic.

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Use adversarial debiasing

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Apply safety filters

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Fine-tune on diverse data

Question 13

To improve factuality in generative AI, which is the best approach?

Accepted Answer

Grounded generation with citations. Grounded generation with citations directly addresses factuality by forcing the model to retrieve and cite verifiable sources (e.g., from a knowledge base or document store) before generating an answer. This approach, often implemented via retrieval-augmented generation (RAG), ensures outputs are anchored to external evidence rather than relying solely on the model's parametric memory, which can produce hallucinations. Citations also enable users to verify claims, making this the most effective technique for improving factual accuracy.

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Set top_p to 0.1

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Reduce output length

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Increase model size

Question 14

A healthcare chatbot must avoid hallucinations. Which TWO techniques should the team implement? (Choose two.)

Accepted Answer

Use chain-of-thought prompting. Chain-of-thought prompting (B) reduces hallucinations by forcing the model to reason step-by-step, which improves factual accuracy and consistency in complex tasks like medical triage. Enabling grounding with a knowledge base (E) anchors the model's output to verified external data, directly preventing fabrication by restricting responses to retrieved facts.

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Set frequency penalty to 0.0

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Use higher temperature

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Increase top_k to 50

Question 15

A financial analyst uses generative AI to summarize earnings reports. The summaries vary in style. Which THREE methods can improve consistency? (Choose three.)

Accepted Answer

Set temperature to 0.2. Setting temperature to 0.2 reduces randomness in token sampling, making the model more deterministic and less likely to produce stylistic variations. Lower temperatures (e.g., 0.1–0.3) narrow the probability distribution, forcing the model to select the most likely next token, which directly improves consistency across multiple summaries.

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Increase max output tokens

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Enable citation mode