Generative AI Leader Practice Test 45 — 15 Questions

Question 1

A marketing team needs to generate personalized email campaigns for thousands of customers. They want to maintain brand tone consistency and avoid manual writing. Which GenAI approach is BEST suited?

Accepted Answer

Use Vertex AI Studio with prompt design and few-shot examples in the prompt. Vertex AI Studio enables prompt engineering with few-shot examples, allowing the team to generate personalized emails while maintaining brand tone consistency without fine-tuning or custom training. This approach leverages a pre-trained large language model (LLM) with carefully designed prompts that include brand guidelines and a few examples, ensuring output adheres to the desired style and context. It avoids the overhead of fine-tuning or building custom models, making it ideal for rapid deployment and iterative refinement.

Answer

Fine-tune a small model on brand guidelines only

Answer

Embed a rules-based template engine with no AI

Answer

Train a custom model from scratch on past campaigns

Question 2

A financial services company is building a customer service agent using Vertex AI Agent Builder. They want the agent to only answer questions based on their approved policy documents, which are stored in Cloud Storage. They also need to ensure that the agent never reveals internal employee names or account numbers. They have set up grounding with the documents but find that the agent sometimes ignores the grounding and generates responses using the model's internal knowledge. What should they do to strictly constrain the agent to only use the provided documents?

Accepted Answer

Use the 'vertex-ai-agent-builder' with strict grounding mode and disable fallback to model knowledge.. Vertex AI Agent Builder offers a 'strict grounding' mode that, when enabled, forces the agent to rely exclusively on the provided grounding documents (e.g., from Cloud Storage) and disables any fallback to the model's internal knowledge. This directly addresses the requirement to prevent the agent from generating responses based on its pre-trained data, ensuring strict adherence to the approved policy documents.

Answer

Add a system instruction that says 'Only answer from the provided documents.'

Answer

Set the model's temperature to 0 and top_p to 0.1.

Answer

Fine-tune the model on the policy documents to limit its knowledge.

Question 3

A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

Accepted Answer

Insufficient training data. Fine-tuning a large language model on Vertex AI with poor validation results is most likely due to insufficient training data. Large language models have billions of parameters and require a substantial amount of high-quality, task-specific data to effectively adapt to a new domain or task; without enough examples, the model cannot learn the desired patterns and will perform poorly on unseen data.

Answer

Incorrect learning rate

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Using wrong model family

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Overfitting due to too many epochs

Question 4

A project manager wants to track the ROI of a generative AI feature that assists customer support agents. Which metric is MOST directly tied to productivity improvement?

Accepted Answer

Average handle time (AHT) per ticket. Average handle time (AHT) directly measures the time agents spend per interaction, so a reduction indicates productivity gain. CSAT measures satisfaction, not efficiency. Cost per API call is a cost metric. Adoption rate measures usage.

Answer

Adoption rate of the AI tool

Answer

Customer satisfaction (CSAT) score

Answer

Cost per API call

Question 5

A data scientist is comparing two fine-tuned models on Vertex AI Model Evaluation. They want to choose the model with better factual accuracy for a medical Q&A task. Which evaluation metric should they prioritize?

Accepted Answer

exact_match. Exact Match (EM) is the correct metric because it measures whether the model's output exactly matches the ground truth answer, which is critical for factual accuracy in medical Q&A where even minor deviations (e.g., 'aspirin' vs. 'acetylsalicylic acid') could indicate incorrect or incomplete knowledge. Vertex AI Model Evaluation supports EM as a binary metric that penalizes any variation, making it ideal for high-stakes domains requiring precise factual recall.

Answer

pairwise_rouge

Answer

ROUGE-L

Answer

BLEU

Question 6

Which Google AI milestone introduced the Transformer architecture that underpins modern LLMs?

Accepted Answer

Transformer paper. The Transformer architecture, which is the foundational technology behind modern large language models (LLMs) like GPT and BERT, was introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. This paper proposed the self-attention mechanism and the encoder-decoder structure that replaced recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, enabling parallelized training and superior handling of long-range dependencies in sequence data.

Answer

AlphaGo

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AlphaFold

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BERT

Question 7

A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Accepted Answer

temperature. Temperature controls the randomness of token selection during generation. A high temperature increases the likelihood of less probable tokens, which can lead to syntactically incorrect code. Lowering temperature makes the model more deterministic and conservative, favoring higher-probability tokens that are more likely to form valid syntax.

Answer

candidate_count

Answer

max_output_tokens

Answer

top_k

Question 8

A company is building a GenAI application using the Gemini API. They want to minimize latency and cost for a high-volume use case. Which strategy is MOST effective?

Accepted Answer

Implement caching for identical or similar user queries. Caching frequent requests reduces redundant processing, reducing both latency and token costs. Batching can also help, but caching directly addresses repeated queries.

Answer

Use a fine-tuned smaller model for the specific task

Answer

Increase temperature to generate more diverse responses

Answer

Use the largest available model for all requests

Question 9

A company is using Vertex AI Model Garden to discover and test various foundation models. They need a model that can generate code from natural language. Which model should they select?

Accepted Answer

Codey. Codey is Google's family of models specifically designed for code generation, including converting natural language descriptions into code. It is built on the PaLM 2 architecture and is optimized for tasks like code completion, code generation, and code chat, making it the correct choice for generating code from natural language.

Answer

Chirp

Answer

Med-PaLM

Answer

Imagen

Question 10

What is the primary purpose of a system instruction in the Gemini API?

Accepted Answer

Define the overall behavior and constraints for the model. The system instruction in the Gemini API is the primary mechanism to define the overall behavior, persona, constraints, and guardrails for the model across all interactions. Unlike per-query parameters, it sets a persistent context that shapes how the model interprets every user prompt, ensuring consistent adherence to rules such as tone, format, or safety policies.

Answer

Set the model's temperature and top_p

Answer

Provide few-shot examples for each query

Answer

Set the maximum output length

Question 11

Which statement best describes the difference between the Gemini Flash and Gemini Pro models on Vertex AI?

Accepted Answer

Gemini Flash is optimized for speed and cost, while Gemini Pro provides higher quality for complex tasks. Gemini Flash is specifically designed for low-latency, high-throughput, and cost-efficient inference, making it ideal for high-volume, simpler tasks. In contrast, Gemini Pro is a larger, more capable model that delivers superior quality and reasoning for complex, multi-step tasks, though at higher latency and cost. This distinction is fundamental to the Gemini model family on Vertex AI, where Flash serves as the lightweight, fast option and Pro as the premium, high-quality option.

Answer

Gemini Flash is a distilled version of Gemini Pro that requires fine‑tuning before use

Answer

Gemini Pro is deployed on Google’s TPU v5p chips, while Flash uses TPU v4

Answer

Gemini Flash is only available for image inputs, while Gemini Pro handles text

Question 12

Which of the following best describes the primary benefit of using Grounding with Google Search when building a GenAI chatbot?

Accepted Answer

It allows the model to access real-time information from the internet to reduce hallucinations. Grounding with Google Search connects the GenAI chatbot to real-time internet data, allowing it to retrieve current facts and events that the model was not trained on. This reduces hallucinations by ensuring responses are based on verified, up-to-date information rather than relying solely on the model's static training data.

Answer

It reduces the model's latency by caching responses

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It enables the model to generate images based on text descriptions

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It provides fine-tuning capabilities for domain-specific data

Question 13

A company is considering monetizing a generative AI-powered product. Which two business models are most common and viable?

Accepted Answer

Pay-per-use based on tokens consumed.. Pay-per-use based on tokens consumed aligns directly with the operational cost structure of generative AI models, where each inference incurs compute and memory costs proportional to the number of tokens processed. This model allows customers to pay only for what they use, making it viable for variable workloads and avoiding upfront commitment, while providers can scale revenue with usage. It is the most common monetization strategy for API-based generative AI services, such as OpenAI's GPT-4 or Anthropic's Claude, where pricing is explicitly tied to token counts.

Answer

Free with advertising.

Answer

One-time license fee for the model.

Answer

Selling user data collected from interactions.

Question 14

A financial services firm wants to deploy generative AI for automated investment advice. They are subject to strict regulatory oversight requiring explainability and audit trails. Which strategy best meets these requirements?

Accepted Answer

Implement human-in-the-loop with full logging of model inputs, outputs, and human decisions.. It directly addresses the regulatory requirements for explainability and audit trails by incorporating human oversight and comprehensive logging. The human-in-the-loop (HITL) mechanism ensures that critical investment decisions are reviewed by qualified professionals, while full logging of model inputs, outputs, and human decisions creates a transparent, auditable record. This approach satisfies financial regulations like MiFID II or SEC rules that mandate explainability and accountability in automated advice systems.

Answer

Fine-tune a model on historical trading data without human review.

Answer

Use a black-box large language model with monitoring.

Answer

Deploy a rule-based system augmented with generative AI for content generation.

Question 15

A machine learning engineer submits the above batch prediction job for a large language model. The job is expected to process 100,000 instances. The job takes much longer than expected. Which change would most likely reduce the execution time?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows Vertex AI Batch Prediction to scale out to more worker replicas, processing the 100,000 instances in parallel. The default maxReplicaCount is often 1 or a low number, which forces sequential or limited parallel processing, causing long execution times. By raising this limit, the job can leverage horizontal scaling to distribute the workload across multiple machines, significantly reducing wall-clock time.

Answer

Increase startingReplicaCount to 10 without changing maxReplicaCount

Answer

Increase the machine type to n1-standard-16

Answer

Decrease the batch size to 1