Generative AI Leader Multiple Select — 50 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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.

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Use only open-source models to ensure transparency

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Automatically delete all user logs after 30 days

Question 7

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.

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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 8

What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

Accepted Answer

They compress text into dense vectors for efficient retrieval.. Embedding models convert text into dense vector representations that capture semantic meaning, enabling efficient similarity search in vector databases. This compression reduces the dimensionality of the data, allowing the RAG system to quickly retrieve the most relevant documents from a large corpus based on vector distance metrics like cosine similarity.

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They allow the model to generate new training data automatically.

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They provide deterministic outputs for the same query.

Question 9

Which TWO statements are true about generative AI models?

Accepted Answer

They are typically pre-trained on large datasets.. Generative AI models, such as GPT-4 or DALL-E, are typically pre-trained on vast, diverse datasets (e.g., terabytes of text or images) using unsupervised or self-supervised learning. This pre-training phase allows the model to learn statistical patterns, grammar, and world knowledge, which is then fine-tuned for specific tasks. Without this large-scale pre-training, the model would lack the foundational understanding needed to generate coherent and contextually relevant outputs.

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They are deterministic by design.

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They always produce the same output for the same input.

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They require no data for training.

Question 10

Which THREE are valid methods to reduce bias in generative AI outputs?

Accepted Answer

Using a more diverse training dataset. Training on a more diverse dataset reduces representational bias by exposing the model to a wider range of demographics, cultures, and perspectives. This directly mitigates the model's tendency to overrepresent majority groups or underrepresent minorities, which is a root cause of biased outputs in generative AI.

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Using only English prompts

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Increasing model size

Question 11

Which TWO techniques are effective for reducing bias in generative AI model outputs?

Accepted Answer

Training on diverse and representative datasets. Training on diverse and representative datasets directly reduces sampling bias and coverage gaps in the training distribution, which are primary sources of stereotypical or skewed outputs. By ensuring the model sees balanced examples across demographics, contexts, and edge cases, it learns more equitable representations and reduces the likelihood of generating biased content.

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Increasing model size to learn more patterns

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Relying solely on post-hoc filters

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Limiting the model to only factual prompts

Question 12

A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Accepted Answer

Enable the 'block offensive content' flag in the model's safety configuration.. Enabling the 'block offensive content' flag directly activates Gemini's built-in safety filters, which are designed to detect and suppress toxic language at the model's output layer. This is an immediate, configuration-level measure that requires no additional training or external services, making it the fastest way to reduce harmful responses in a production chatbot.

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Increase the model's temperature to make outputs more conservative.

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Use a separate language model to rephrase the outputs before sending to users.

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Fine-tune the model on a curated dataset of polite conversations.

Question 13

Which TWO factors are most critical when deciding to build a custom GenAI model vs. using a pre-built API? (Select two.)

Accepted Answer

Availability of in-house ML talent. Building a custom GenAI model requires specialized machine learning expertise, including proficiency in frameworks like PyTorch or TensorFlow, experience with distributed training (e.g., using Horovod or DeepSpeed), and the ability to fine-tune architectures like transformers. Without in-house ML talent, the organization cannot effectively manage data curation, hyperparameter tuning, or model evaluation, making a pre-built API the more viable choice. This factor directly determines whether the organization has the technical capacity to undertake custom development.

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Number of layers in the model

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Brand reputation of the model provider

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Volume of expected inference requests

Question 14

Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Accepted Answer

Enable automatic scaling to handle variable traffic. Vertex AI's automatic scaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring low latency during spikes and cost efficiency during lulls. This is a best practice for production workloads where traffic patterns are unpredictable, as it eliminates the need for manual capacity planning.

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Disable logging to reduce cost

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Manually scale instances based on expected load

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Serve the model directly without optimization

Question 15

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

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One-click deployment to a Vertex AI endpoint

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Fine-tuning models directly in the interface

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Building conversational agents with drag-and-drop

Question 16

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Accepted Answer

Slot filling. Slot filling is correct because it allows the agent to collect required parameters (slots) from the user during a conversation, enabling multi-turn interactions to fulfill complex requests. In Vertex AI Agent Builder, slot filling is a core feature for conversational agents, as it systematically prompts for missing information (e.g., date, location) until all necessary slots are filled, enhancing the agent's ability to handle dynamic user inputs.

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Sentiment analysis

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Code execution

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Knowledge base integration

Question 17

Which THREE factors should be considered when choosing between Gemini 1.5 Pro and Gemini 1.5 Flash for a customer-facing chatbot? (Choose three.)

Accepted Answer

Cost constraints: Flash is more cost-effective per token. Gemini 1.5 Flash is designed as a cost-optimized model, offering significantly lower per-token pricing compared to Gemini 1.5 Pro. For a customer-facing chatbot with high query volumes, cost efficiency is a primary consideration, making Flash the more economical choice for routine interactions.

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Safety filters: Pro has stricter safety defaults

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Multimodal capability: Flash does not support image input

Question 18

Which TWO options are benefits of using Vertex AI Model Garden compared to using raw pre-trained models from external sources? (Choose two.)

Accepted Answer

Integration with Vertex AI tools like evaluation and monitoring. Vertex AI Model Garden is deeply integrated with the Vertex AI ecosystem, providing native access to tools like Vertex AI Evaluation for model performance assessment and Vertex AI Monitoring for drift detection and observability. This integration eliminates the need for custom pipelines to connect external models with these managed services, streamlining the MLOps workflow.

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Lower cost compared to using generic APIs

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Ability to fine-tune models on custom data

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Guaranteed data privacy and no data sharing

Question 19

A company is deploying a generative AI system for medical diagnosis. Which TWO measures are essential for responsible AI in this high-stakes domain?

Accepted Answer

Ensure a human medical professional reviews all AI-generated diagnoses. High-stakes domains require human oversight and transparency. Human review ensures accountability, and Model Cards communicate capabilities and limitations to stakeholders.

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Allow the AI to make autonomous decisions in time-sensitive emergencies

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Publish all patient data used for training to ensure transparency

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Use the AI only for administrative tasks, not diagnosis

Question 20

Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.

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Cloud SQL

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Bigtable

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Cloud Functions