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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
Practice 10 questionsBrowse domain →

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 1easymultiple 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 2mediummultiple 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 3easymultiple choice
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A team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?

Question 4hardmultiple 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 5mediummultiple choice
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A media company is using Vertex AI's Imagen model to generate images for marketing campaigns. They have a set of prompts that describe desired scenes, but the generated images often contain artifacts such as distorted faces or unnatural lighting. The team has tried varying the prompt wording but the issues persist. They are using the default parameters (no modifications). They have a budget for additional compute resources and want to improve image quality without switching to a more expensive model. The team has access to a small set of high-quality images in the same style as their target outputs. What should the team do?

Question 6easymultiple choice
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A company notices that their AI chatbot occasionally generates incorrect information. Which technique can best reduce hallucinations without retraining?

Question 7mediummultiple choice
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A team wants to improve the factual accuracy of their chatbot responses regarding internal company policies. What is the most effective approach?

Question 8hardmultiple choice
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A real-time customer support chatbot using Gemini is experiencing high latency. The team must maintain response quality while improving speed. Which technique should they implement?

Question 9easymultiple choice
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A marketing company wants to fine-tune a generative AI model to adopt a specific brand voice. Which tuning method is most appropriate?

Question 10mediummultiple choice
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Despite applying safety filters, a generative AI model still produces toxic outputs in some cases. Which additional technique should be applied?

Question 11hardmultiple choice
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For a document summarization task, a team wants to produce concise summaries without losing key information. Which combination of techniques is most effective?

Question 12hardmulti select
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Which THREE are best practices for designing prompts for a generative AI model?

Question 13easymulti select
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Which TWO are advantages of using Retrieval-Augmented Generation (RAG) over fine-tuning?

Question 14mediummultiple choice
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Refer to the exhibit. A team attempted to start a model tuning job but received the error 'Quota limit exceeded for tuning jobs in region us-central1'. What is the most appropriate action?

Exhibit

gcloud ai tuning-jobs create \
  --project=my-project \
  --region=us-central1 \
  --model=gemini-1.5-pro-001 \
  --tuned-model-display-name=test-tune \
  --training-data=gs://my-bucket/data.jsonl \
  --model-serving-regions=us-east1
Question 15hardmultiple choice
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Refer to the exhibit. A Vertex AI endpoint configured with the above deployment is returning HTTP 429 (Too Many Requests) errors during peak traffic. The current CPU utilization reaches 80% consistently. What should the team adjust to resolve this?

Exhibit

{
  "dedicatedEndpoints": 1,
  "machineType": "n1-standard-2",
  "minReplicaCount": 1,
  "maxReplicaCount": 5,
  "scaleTarget": 0.5
}
Question 16easymultiple choice
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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?

Question 17hardmultiple choice
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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?

Question 18easymultiple choice
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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?

Question 19mediummultiple choice
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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?

Question 20hardmultiple choice
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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?

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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.