Generative AI Leader Practice Test 48 — 15 Questions

Question 1

A developer is using Vertex AI's Generative AI Studio to prototype a text summarization model. The initial results are too verbose. What is the most efficient way to adjust the output length without retraining?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Modify the prompt with specific length instructions and adjust model parameters. The most efficient way to adjust output length without retraining is to modify the prompt with specific length instructions (e.g., 'Summarize in one sentence') and adjust model parameters like max output tokens, temperature, and top_p in Vertex AI's Generative AI Studio. Option A is wrong because switching to a smaller base model like BERT would require retraining or different model architecture, and BERT is not primarily a text generation model for summarization. Option B is wrong because adding a separate classifier increases complexity and does not directly control the model's output length. Option C is wrong because fine-tuning requires additional training data and computational resources, which is not the most efficient approach compared to prompt and parameter adjustments.

Answer

Switch to a smaller base model like BERT

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Use a separate classifier to filter long responses

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Fine-tune the model with a dataset of concise summaries

Question 2

Which THREE are essential components of a responsible AI strategy for GenAI? (Select three.)

Accepted Answer

Human oversight for critical decisions. Human oversight for critical decisions (C) is essential because GenAI models can produce plausible but incorrect or harmful outputs. A responsible AI strategy mandates that a human-in-the-loop reviews high-stakes outputs, such as medical diagnoses or financial approvals, to prevent automated errors from causing real-world harm. This aligns with the principle of human accountability in AI governance frameworks like the NIST AI Risk Management Framework.

Answer

Use of only open-source models

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Maximum model size

Question 3

A developer deployed a large language model on Vertex AI for real-time chat. Users report slow response times. The model generates sentences one word at a time. Which optimization should be applied to reduce latency?

Accepted Answer

Use streaming responses to start output earlier.. Streaming responses allow the model to send tokens to the client as they are generated, rather than waiting for the full sequence to complete. This reduces perceived latency significantly in real-time chat, as users see the first word appear almost immediately, even though the total generation time remains similar.

Answer

Batch multiple user queries together.

Answer

Deploy the model with more accelerators.

Answer

Enable prompt caching to reuse previous queries.

Question 4

A company is evaluating whether to build a custom fine-tuned model for code generation or use a pre-built API like Gemini API. The code generation needs to follow the company's internal coding standards. Which consideration is MOST important in deciding to build vs buy?

Accepted Answer

Pre-built APIs may not generate code that adheres to internal standards without extensive prompting. The core build-vs-buy decision hinges on whether a pre-built API can meet the specific requirement of adhering to internal coding standards. General-purpose APIs like Gemini are trained on public code and will not inherently follow a company's proprietary style guides, naming conventions, or architectural patterns without extensive prompt engineering or fine-tuning. This makes option A the most decision-relevant consideration.

Answer

Pre-built APIs are always cheaper than fine-tuned models

Answer

Fine-tuned models have lower latency than pre-built APIs

Answer

Data privacy is easier to achieve with pre-built APIs

Question 5

A financial institution is deploying a generative AI solution that generates investment advice. They must ensure fairness, avoid toxic outputs, and comply with regulations like GDPR. Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Use Vertex AI Safety Attributes to filter harmful content in both input and output.. Vertex AI Safety Attributes provides built-in safety filters that can detect and block harmful content (e.g., hate speech, toxicity, financial misinformation) in both user prompts and model outputs. This directly addresses the need to avoid toxic outputs and comply with regulations like GDPR, which require protecting users from harmful or biased advice.

Answer

Set the model temperature to 0 to eliminate creativity and reduce bias.

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Fine-tune the model exclusively on compliant financial documents.

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Disable request logging to avoid storing sensitive data.

Question 6

A generative AI model for chatbot responses sometimes produces toxic language. The team wants to reduce toxicity without significantly affecting the model's helpfulness. Which approach is best?

Accepted Answer

Fine-tune with a dataset of non-toxic responses and use RLHF. Fine-tuning with a curated dataset of non-toxic responses directly adjusts the model's weights to reduce the likelihood of generating toxic language, while RLHF (Reinforcement Learning from Human Feedback) further aligns the model with human preferences for helpfulness and safety. This combined approach addresses the root cause of toxicity in the model's behavior without the blunt trade-offs of other methods, preserving the model's utility.

Answer

Increase the temperature parameter

Answer

Reduce the maximum output tokens

Answer

Apply a toxicity classifier as a post-processing filter

Question 7

A healthcare startup needs to process medical claims and extract structured data (e.g., patient name, procedure codes, amounts) from scanned PDF forms. They require HIPAA compliance and prefer a pre-built Google Cloud service. Which service should they use?

Accepted Answer

Document AI Healthcare API with the Claims processor. The Document AI Healthcare API with the Claims processor is a pre-built, HIPAA-compliant Google Cloud service specifically designed to extract structured data (e.g., patient name, procedure codes, amounts) from scanned medical claim forms. It leverages specialized machine learning models trained on healthcare documents, ensuring accurate parsing of fields like CPT codes and ICD-10 codes while meeting regulatory requirements.

Answer

Cloud Vision API with OCR

Answer

Vertex AI AutoML Tables

Answer

Natural Language AI with entity extraction

Question 8

A retail company with a large FAQ database wants to build a generative AI customer service chatbot that can answer questions accurately with up-to-date information. Which business strategy should they prioritize?

Accepted Answer

Use retrieval-augmented generation (RAG) with vector search on the FAQ database.. Retrieval-augmented generation (RAG) with vector search allows the chatbot to dynamically retrieve the most relevant, up-to-date FAQ entries from a large database at inference time, grounding the generative model's responses in verified content without requiring retraining. This approach combines the flexibility of a pre-trained language model with the accuracy of real-time information retrieval, ensuring answers reflect the latest FAQ updates.

Answer

Train a new model from scratch using the FAQ data.

Answer

Fine-tune a foundational model on the entire FAQ dataset.

Answer

Use a general-purpose language model without any customization.

Question 9

A financial services firm needs to generate synthetic data for training models while ensuring that no real customer data leaks. Which technique should they use?

Accepted Answer

Differential privacy during fine-tuning. Differential privacy during fine-tuning is the correct technique because it adds calibrated noise to the training process, ensuring that the synthetic data generated does not reveal information about any individual real customer record. This approach provides a formal mathematical guarantee of privacy, making it suitable for generating synthetic data that preserves statistical properties while preventing data leakage. In contrast, other methods like redaction, masking, or using a public model do not inherently prevent the model from memorizing and reproducing sensitive information.

Answer

Using the Vertex AI PII redaction service

Answer

Using a public foundation model without fine-tuning

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Data masking before training

Question 10

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on their internal knowledge base. Which feature should they use?

Accepted Answer

Grounding with enterprise data stores. Vertex AI Agent Builder supports grounding with enterprise data stores, which allows the agent to retrieve and answer questions based on the company's internal knowledge base (e.g., documents, PDFs, websites) without relying on public web search. This ensures responses are grounded in proprietary, controlled data, making it the correct choice for a customer support chatbot that needs to reference internal policies or product documentation.

Answer

Grounding with Google Search

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Model tuning

Answer

Prompt engineering

Question 11

Which of the following best describes how large language models (LLMs) generate text?

Accepted Answer

They predict the next token in a sequence based on the preceding tokens. LLMs are trained to predict the next token given the preceding tokens. During inference, they generate one token at a time autoregressively.

Answer

They retrieve the most similar text from a database and return it

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They use a rule-based grammar engine to construct sentences

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They randomly select words from a fixed vocabulary

Question 12

A machine learning engineer is tuning a large language model on Vertex AI for question answering. They want to evaluate the model's performance before deployment. Which THREE metrics should they consider?

Accepted Answer

F1 score. For a question-answering evaluation on Vertex AI, F1 score (B) is correct because it measures token-level overlap between the predicted answer and the ground-truth answer, balancing precision and recall and handling partial matches. Exact match (C) is correct because it reports the percentage of predictions that exactly equal the reference answer, a standard metric in QA benchmarks such as SQuAD. ROUGE-L score (E) is correct because it measures the longest common subsequence between generated and reference text, capturing fluency and recall-oriented overlap useful for generative QA outputs. Cost per training epoch (A) and training time per epoch (D) are operational/training-efficiency measures, not model quality metrics, so they do not evaluate performance before deployment.

Answer

Cost per training epoch

Answer

Training time per epoch

Question 13

A data scientist is using Vertex AI to fine-tune a Gemini model for a specialized legal document summarization task. They have a small set of labeled examples (200 pairs). Which fine-tuning method is MOST cost-effective and likely to perform well?

Accepted Answer

Adapter-based fine-tuning (e.g., LoRA). Adapter-based fine-tuning (like LoRA) updates only a small fraction of parameters, making it efficient with small datasets and low cost, while still adapting the model to the task.

Answer

Full fine-tuning of all model parameters

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Training a small custom model from scratch

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Prompt engineering with few-shot examples only

Question 14

A developer is using Gemini 1.5 Pro and needs to process a 2-hour video to answer questions about its content. The video is stored in Cloud Storage. What is the most efficient approach?

Accepted Answer

Send the video file as part of the prompt to Gemini 1.5 Pro. Gemini 1.5 Pro supports video input natively; you can pass the video directly (via GCS URI) and ask questions. Transcribing first adds latency and loses visual context.

Answer

Extract frames using Video Intelligence API and then send them as images to Gemini

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Transcribe the video using Chirp, then analyze the text with Gemini

Answer

Use a custom model fine-tuned on video understanding tasks

Question 15

A data scientist needs to generate high-quality images from text prompts using Google Cloud. Which service should they use?

Accepted Answer

Imagen. Imagen is Google Cloud's text-to-image diffusion model. PaLM 2 and Gemini are primarily text models; Codey is for code generation.

Answer

PaLM 2

Answer

Gemini Pro Vision

Answer

Codey