Generative AI Leader Practice Exam 1 — 20 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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

Apply conditional formatting rules based on patterns

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Create charts from images pasted into the sheet

Answer

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.

Answer

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.

Answer

Implement custom safety filters on the model outputs

Answer

Use a larger context window to capture all interactions

Answer

Train an internal model from scratch to replace the vendor model

Question 11

A financial services firm is deploying a generative AI chatbot for customer inquiries. They have strict compliance requirements: all conversations must be auditable and the model must not use customer data for training. Which Google Cloud offering should they choose?

Accepted Answer

Vertex AI Agent Builder with data governance controls. Vertex AI Agent Builder is correct because it provides built-in data governance controls that prevent customer data from being used for model training, while also supporting full auditability through integration with Cloud Audit Logs and Cloud Logging. This directly addresses the firm's compliance requirements for auditable conversations and data privacy.

Answer

Private Google Access for on-premises connectivity

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Dialogflow CX with Cloud Logging

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Cloud AI Platform Pipelines

Question 12

A company is developing a generative AI application that will be used by customers in the EU. To comply with the GDPR and the upcoming EU AI Act, which THREE measures should they implement? (Select 3)

Accepted Answer

Store and process all user data within the EU or in regions with adequacy decisions. GDPR requires data residency/local processing (measures 1), explanation of AI decisions (right to explanation) (measure 3), and transparency about AI interaction (measure 5). The other options are not mandated by these regulations.

Answer

Use only open-source models to ensure transparency

Answer

Automatically delete all user logs after 30 days

Question 13

A team wants to fine-tune a PaLM 2 model with their own data on Vertex AI. What is the recommended way to prepare the training data?

Accepted Answer

JSON Lines file with 'input_text' and 'output_text' keys. The recommended format for fine-tuning PaLM 2 on Vertex AI is a JSON Lines file with 'input_text' and 'output_text' keys. This structure directly maps to the model's expected supervised fine-tuning (SFT) input, where each line represents a single training example with a prompt and its corresponding response. Vertex AI's fine-tuning pipeline is optimized to parse this schema, ensuring correct tokenization and alignment during training.

Answer

TFRecord files

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CSV file with prompt and completion columns

Answer

Pickle serialized objects

Question 14

A marketing agency wants to generate images using Imagen on Vertex AI. They need to ensure the images are unique and avoid copyright issues. Which parameter adjustment is most relevant?

Accepted Answer

Use negative prompts. Negative prompts allow the model to exclude specific concepts, styles, or elements from generated images, directly reducing the risk of replicating copyrighted or trademarked content. By explicitly telling Imagen what not to include, the agency can steer outputs away from protected works without needing to modify training data or safety filters.

Answer

Increase training steps

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Increase seed variability

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Set safety threshold

Question 15

A research team wants to generate high-quality images from text descriptions for a marketing campaign. They need the ability to edit specific regions of generated images while preserving the rest. Which Google Cloud AI service should they use?

Accepted Answer

Imagen on Vertex AI. Imagen supports inpainting (region editing via masks) natively. Gemini is multimodal but not primarily for image generation. Veo generates video, not images. Codey is for code.

Answer

Gemini 1.5 Pro with image input

Answer

Codey on Vertex AI

Answer

Veo on Vertex AI

Question 16

A legal firm wants to use a generative AI model to draft contract clauses. They need to ensure the model's outputs cite specific legal precedents and statutes, and that the reasoning behind each clause is transparent. Which combination of explainability techniques should they prioritize?

Accepted Answer

Grounding and chain-of-thought reasoning

Answer

Confidence indicators and model cards

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Content safety filters and human oversight

Answer

Datasheets for Datasets and PAIR Explorables

Question 17

A developer needs to integrate a GenAI model into an existing customer relationship management (CRM) system. The CRM exposes REST APIs and runs on-premises. Which integration pattern is MOST suitable?

Accepted Answer

Implement an API-first integration by calling Vertex AI API from the CRM's backend. API-first integration using Vertex AI API allows any system with HTTP capabilities to call the GenAI model. Workspace add-ons and Apps Script are for Google Workspace only. Fine-tuning doesn't help with integration.

Answer

Use Apps Script to call the model from Google Sheets

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Fine-tune a model on CRM data and deploy it on-premises

Answer

Build a custom Google Workspace add-on

Question 18

What is the primary purpose of the temperature parameter when generating text with an LLM?

Accepted Answer

It controls the randomness or creativity of the output. Temperature controls the randomness of token selection. Higher temperature increases creativity, lower temperature makes outputs more deterministic.

Answer

It sets the maximum number of tokens in the response

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It determines the number of most likely tokens considered at each step

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It sets the cumulative probability threshold for token selection

Question 19

A data scientist wants to apply reinforcement learning from human feedback (RLHF) to improve a chatbot's helpfulness. Which TWO steps are part of the RLHF process? (Select 2)

Accepted Answer

Collect human rankings or preferences on multiple model outputs. RLHF typically involves collecting human rankings of model outputs and then training a reward model to score outputs, which is used to fine-tune the model via PPO.

Answer

Use prompt engineering to tune the model without retraining

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Collect human-annotated demonstrations of ideal responses

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Deploy the model in A/B testing to gather implicit feedback

Question 20

A developer is building a code‑generation assistant using the Codey API on Vertex AI. The assistant should generate Python functions based on natural language descriptions. However, the generated code sometimes contains syntax errors. Which parameter adjustment would MOST directly help reduce syntax errors?

Accepted Answer

Lower the temperature (e.g., from 0.8 to 0.2). Reducing temperature makes the model more deterministic, which typically reduces creative but incorrect outputs like syntax errors. Prompt engineering can also help, but adjusting temperature is the simplest direct fix. Increasing max tokens or changing top-k does not directly address syntax correctness.

Answer

Increase the context window

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Set top-k to 1

Answer

Increase the max output tokens