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Techniques to Improve Generative AI Model Output practice questions

Practise Google Cloud Generative AI Leader Generative AI Leader Techniques to Improve Generative AI Model Output practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: Techniques to Improve Generative AI Model Output
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What the exam tests

What to know about Techniques to Improve Generative AI Model Output

Techniques to Improve Generative AI Model Output questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Techniques to Improve Generative AI Model Output exam traps

  • ▸Answering from memory before reading the full scenario.
  • ▸Missing a constraint such as cost, availability, security, scope or command context.
  • ▸Choosing a broad answer when the question asks for the most specific fix.
  • ▸Ignoring why the wrong options are tempting.

Practice set

Techniques to Improve Generative AI Model Output questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice
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A team is building a generative AI model for customer support. They notice the model often produces overly polite but unhelpful responses. Which technique would best improve response quality without sacrificing helpfulness?

Question 2hardmultiple choice
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A generative AI model for code generation sometimes produces syntactically incorrect code. The team wants to reduce syntax errors without retraining the entire model. Which approach is most effective?

Question 3easymultiple choice
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A company uses a text-to-image model to generate marketing visuals. The outputs often contain distorted human faces. Which technique is most likely to improve face generation?

Question 4mediummultiple choice
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A team is deploying a large language model for legal document summarization. They find the model occasionally omits critical legal clauses. Which improvement technique would be most effective?

Question 5hardmultiple choice
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A generative AI model for chatbot responses sometimes produces toxic language. The team wants to reduce toxicity without significantly affecting the model's helpfulness. Which approach is best?

Question 6easymultiple choice
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A team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?

Question 7mediummultiple choice
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A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Question 8hardmulti select
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A team is fine-tuning a large language model for medical advice. Which TWO techniques are most effective for improving the safety and reliability of the model's outputs?

Question 9mediummulti select
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A team wants to reduce hallucinations in a question-answering model. Which THREE techniques should they consider?

Question 10easymulti select
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Which TWO techniques are commonly used to control the style and tone of a generative model's output?

Question 11hardmultiple choice
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Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?

Exhibit

Refer to the exhibit.

```
# Model configuration before change
model = GenerativeModel("text-bison@002")
response = model.generate(
    prompt="Summarize the following article: ...",
    temperature=0.7,
    top_k=40,
    top_p=0.95
)
# After change
model = GenerativeModel("text-bison@002")
response = model.generate(
    prompt="Summarize the following article: ...",
    temperature=0.2,
    top_k=10,
    top_p=0.85
)
```
Question 12mediummultiple choice
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Refer to the exhibit. The endpoint is experiencing high latency during traffic spikes. The team wants to improve response time by reducing queueing. Which change to the configuration would be most effective?

Exhibit

Refer to the exhibit.

```
# Vertex AI Endpoint configuration
{
  "model": "gemini-1.5-pro",
  "endpoint": "projects/my-project/locations/us-central1/endpoints/123456789",
  "deployedModel": {
    "modelVersion": "1",
    "minReplicaCount": 1,
    "maxReplicaCount": 5,
    "autoscalingMetricSpecs": [
      {
        "metricName": "custom.googleapis.com|genai|request_count",
        "target": 100
      }
    ]
  }
}
```
Question 13mediummultiple choice
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A data science team is fine-tuning a large language model using Vertex AI to generate marketing copy. They notice that the generated text is often repetitive and lacks creativity. Which technique should they apply to improve output diversity?

Question 14hardmultiple choice
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A team is using Vertex AI Pipelines to deploy a generative AI model for real-time inference. The model sometimes generates harmful content. They want to implement a safety filter that checks the output before returning it to the user, but they need to minimize latency. Which approach best balances safety and performance?

Question 15easymultiple choice
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A developer is using the Gemini API to build a chatbot. They want the model to always respond in a friendly, professional tone. Which prompt engineering technique should they use?

Question 16mediummultiple choice
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A team is using a pre-trained language model to summarize legal documents. They find that summaries often miss key dates and parties involved. Which technique would most effectively improve factual accuracy?

Question 17hardmultiple choice
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A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining fluency. They have a large dataset of previous support conversations. Which strategy should they prioritize?

Question 18easymultiple choice
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A developer is using Vertex AI Studio to test prompts for a text generation model. They want the model to follow a specific output format (JSON). Which prompt engineering approach is most effective?

Question 19mediummulti select
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Which TWO techniques can help improve the factual accuracy of a language model's outputs? (Choose two.)

Question 20hardmulti select
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Which THREE approaches are effective for reducing bias in generative model outputs? (Choose three.)

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Frequently asked questions

What does the Generative AI Leader exam test about Techniques to Improve Generative AI Model Output?
Techniques to Improve Generative AI Model Output questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Techniques to Improve Generative AI Model Output questions in a focused session?
Yes — the session launcher on this page draws every question from the Techniques to Improve Generative AI Model Output domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other Generative AI Leader topics?
Use the topic links above to move to related areas, or go back to the Generative AI Leader question bank to see all topics.
Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the Generative AI Leader exam covers. They are not copied from any real exam or dump site.