Generative AI Leader Practice Test 1 — 10 Questions

Question 1

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?

Accepted Answer

Set system instructions to 'You are a friendly and professional assistant.'. Setting system instructions is the most direct and reliable way to define the model's persona and behavioral constraints. In the Gemini API, system instructions act as a persistent, top-level directive that influences every response, ensuring the chatbot consistently adopts a friendly and professional tone without requiring repeated examples or parameter tuning.

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Include a few-shot example in every user message.

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Set the temperature to 0.2.

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Set max output tokens to 100.

Question 2

What are THREE best practices for responsible generative AI deployment?

Accepted Answer

Monitor model performance and data drift over time. Continuous monitoring of model performance and data drift is essential for maintaining the reliability and safety of generative AI systems. Data drift occurs when the statistical properties of input data change over time, which can degrade model accuracy and introduce unintended biases. Regular monitoring allows teams to detect these shifts early and retrain or adjust the model to sustain responsible behavior.

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Maximize model size for best accuracy

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Avoid fine-tuning the model to preserve original capabilities

Question 3

A company wants to integrate generative AI into their existing CRM workflow to draft personalized email responses. They have limited engineering resources. Which two approaches should they consider? (Choose TWO)

Accepted Answer

Use Vertex AI API with a low-code integration platform (e.g., Apigee). Using Gemini API via Apps Script is a lightweight integration, and using Vertex AI API with a low-code tool like Apigee or Cloud Functions can also minimize engineering effort. Building a custom UI or fine-tuning is resource-intensive.

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Fine-tune a model on historical email data to ensure brand voice

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Deploy a dedicated GPU cluster for inference

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Build a custom web UI for the assistant from scratch

Question 4

A company is designing a prompt engineering strategy for a customer service chatbot using Gemini. Which two practices are recommended for improving response quality? (Choose TWO)

Accepted Answer

Use chain-of-thought prompting. Chain-of-thought prompting (A) is recommended because it guides the model to reason step-by-step, improving accuracy on complex customer service queries by breaking down multi-step problems. This technique leverages Gemini's ability to follow logical sequences, reducing errors in tasks like troubleshooting or escalation decisions.

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Always provide multiple examples in the prompt

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Avoid any context in the prompt

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Set temperature to 1.0 for maximum creativity

Question 5

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).. 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.

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

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

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

Question 6

A team is tuning a large language model for a question-answering task. They notice the model gives high confidence scores to answers that are factually incorrect. Which evaluation metric should they primarily use to detect this overconfidence problem?

Accepted Answer

Expected Calibration Error (ECE). Expected Calibration Error (ECE) directly measures the alignment between a model's predicted confidence and its actual accuracy. In this scenario, high confidence on incorrect answers indicates miscalibration, and ECE quantifies this mismatch by binning predictions by confidence and computing the average absolute difference between accuracy and confidence per bin.

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Perplexity

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BLEU score

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ROUGE-L

Question 7

Which TWO actions are recommended best practices for cost optimization when deploying generative AI models on Vertex AI?

Accepted Answer

Use batch prediction for non-real-time workloads. Batch prediction processes predictions asynchronously in large batches, which is significantly more cost-effective than online (real-time) prediction for workloads that do not require immediate responses. Vertex AI batch prediction jobs automatically scale down to zero when not in use, eliminating idle compute costs, and you only pay for the resources consumed during the job execution.

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Deploy the model in a single region to reduce network costs

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Store all model prediction logs indefinitely for auditing

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Always use GPU instances for inference

Question 8

A data analyst wants to use Gemini for Google Workspace to assist with Sheets. Which two capabilities does Duet AI in Sheets provide? (Choose TWO)

Accepted Answer

Generate formula suggestions based on natural language descriptions. Duet AI in Sheets can interpret natural language descriptions of desired calculations and automatically generate the corresponding formula. This leverages Gemini's language understanding to translate user intent into spreadsheet functions, significantly reducing the need to manually recall or construct complex formulas.

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Apply conditional formatting rules based on patterns

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Create charts from images pasted into the sheet

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Write custom Apps Script code automatically

Question 9

A team set a budget alert for their GenAI API usage at $10,000. They received the alert with current spend of $12,500. Which business action is most appropriate as a first step?

Accepted Answer

Review usage patterns and optimize prompt lengths and frequencies. The first step in responding to a budget overrun should be to analyze usage patterns and optimize prompt lengths and frequencies. This approach identifies inefficiencies (e.g., unnecessarily verbose prompts, excessive retries) that directly reduce token consumption and cost without disrupting critical operations. It aligns with the principle of cost optimization before making architectural or policy changes.

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Pause all non-critical use cases immediately

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Switch to a cheaper model provider

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Increase the budget by 50% to $15,000

Question 10

A company is required by the EU AI Act to ensure high-risk AI systems are transparent and auditable. They are using a proprietary model from a vendor. Which step is CRITICAL?

Accepted Answer

Ask the vendor to provide a Model Card and datasheets for the training data. The vendor must provide documentation (e.g., model cards, datasheets) to enable transparency and audit. Auditing the vendor's training data is not possible without cooperation. The other options are internal measures that do not address vendor transparency.

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Implement custom safety filters on the model outputs

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Use a larger context window to capture all interactions

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Train an internal model from scratch to replace the vendor model