Generative AI Leader Practice Test 49 — 15 Questions

Question 1

A company is building a multilingual customer support chatbot that needs to understand and respond in 20 languages. Which Google model is most suitable for this task?

Accepted Answer

Gemini. Gemini is a multimodal large language model (LLM) designed for understanding and generating text across multiple languages, making it the most suitable choice for a multilingual customer support chatbot. Unlike specialized models, Gemini's architecture supports over 100 languages natively, enabling it to handle the 20-language requirement without needing separate language-specific models.

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Chirp

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Imagen

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Codey

Question 2

A company wants to use generative AI to create short product videos from text descriptions. Which Google Cloud service should they consider?

Accepted Answer

Veo. Veo is Google's video generation model that creates videos from text. Imagen creates images, Chirp creates audio, and Gemini is multimodal but not specialized for video generation.

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Chirp

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Imagen

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Gemini

Question 3

A developer is building a code generation assistant using Codey. They notice that the generated code sometimes contains deprecated API calls. What is the most likely cause?

Accepted Answer

Codey's training data has a knowledge cutoff date before the deprecation. Codey, like all large language models, is trained on a static dataset with a specific knowledge cutoff date. If the training data predates the deprecation of certain APIs, the model will not be aware of the newer, recommended alternatives and will continue to generate code using the deprecated calls. This is a fundamental limitation of the model's training data recency, not a parameter tuning issue.

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The top-p sampling is too low, limiting the model's vocabulary

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The temperature setting is too high, causing creative but incorrect outputs

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The context window is too short to include relevant API documentation

Question 4

Which TWO statements are true about generative AI models?

Accepted Answer

They are typically pre-trained on large datasets.. Option A is correct because generative AI models such as large language models and diffusion models undergo a pre-training phase on massive, broad datasets (e.g., web text, images) before any fine-tuning or alignment, which is what gives them general-purpose capabilities. Option D is correct because these models learn the underlying probability distribution of the training data and can sample from it to produce novel outputs—new sentences, images, or code—that were not present verbatim in the training set. Option B is incorrect because generative models are inherently stochastic; sampling steps such as temperature, top-k, or top-p introduce randomness, so they are not deterministic by design. Option C is incorrect for the same reason: with non-zero temperature or random sampling, the same input can yield different outputs across runs. Option E is incorrect because training data is essential—without a corpus to learn from, the model would have no parameters or distribution to generate from.

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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 5

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. Option C is correct because RLHF fundamentally relies on gathering human preference data, typically by having annotators rank or compare multiple candidate responses from the model, which produces the preference signal used to define what 'helpful' means. Option E is correct because those human preferences are used to train a separate reward model that learns to predict human judgments, and this reward model then supplies the scalar reward signal that the policy (the chatbot) is optimized against with reinforcement learning. Options A, B, and D are not part of the core RLHF pipeline: prompt engineering (A) tunes behavior without retraining and involves no reward learning, collecting ideal demonstrations (B) is supervised fine-tuning rather than preference-based RLHF, and A/B testing in deployment (D) is an evaluation/production feedback technique, not a training step in the RLHF process.

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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 6

A company is deploying a chatbot that uses a foundation model. They want to minimize latency for user queries. Which action is most effective?

Accepted Answer

Use a smaller distilled model. Distilled models are smaller, faster versions of larger foundation models, trained to mimic their behavior while requiring fewer computational resources. This directly reduces inference latency because fewer parameters mean faster forward passes through the network, which is critical for real-time chatbot responses.

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Use a larger model with more parameters

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Disable safety filters

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Increase the number of tokens

Question 7

An organization wants to ensure their generative AI application does not produce toxic or harmful content. Which Vertex AI feature should they implement?

Accepted Answer

Safety filters and content moderation. Safety filters and content moderation in Vertex AI allow organizations to define and enforce policies that block or flag toxic, harmful, or inappropriate content generated by the model. This feature uses pre-built and customizable classifiers to evaluate prompts and responses against safety attributes (e.g., hate speech, harassment, sexually explicit content) before returning them to the user, directly addressing the requirement to prevent harmful outputs.

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Explainable AI

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AutoML

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Model Monitoring

Question 8

A team uses PaLM 2 API to generate product descriptions, but the output sometimes contains factual inaccuracies. What is the best approach to improve accuracy?

Accepted Answer

Use grounding with Google Search. Grounding with Google Search is the correct approach because it allows the PaLM 2 API to retrieve real-time, verifiable information from the web, directly reducing factual inaccuracies in generated product descriptions. Unlike parameter adjustments, grounding provides an external knowledge source that the model can cite, ensuring outputs are based on current and accurate data rather than relying solely on its training data.

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Increase the temperature parameter

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Reduce the top_k value

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Set the max_output_tokens higher

Question 9

A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?

Accepted Answer

A text generation model (e.g., text-bison@001).. Text-bison@001 is a dedicated text generation model optimized for tasks like summarization, translation, and content creation from structured inputs. It can take a list of features as a prompt and generate coherent, descriptive product descriptions without needing conversational context or code-specific outputs.

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An embedding model (e.g., textembedding-gecko@001).

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A chat model (e.g., chat-bison@001).

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A code generation model (e.g., code-bison@001).

Question 10

An e-commerce company is using a generative AI model to recommend products. They notice that the recommendations are often irrelevant. What is the most likely cause?

Accepted Answer

Inadequate prompt engineering. Inadequate prompt engineering is the most likely cause because generative AI models rely heavily on the quality and specificity of the input prompt to produce relevant outputs. If the prompts used to generate product recommendations are vague, poorly structured, or lack context (e.g., not including user preferences or historical behavior), the model will return generic or irrelevant suggestions. This is a common failure point in recommendation systems where the prompt acts as the primary interface for steering model behavior.

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Using an outdated model version

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Incorrect regional endpoint configuration

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Overfitting on training data

Question 11

A media company is using a generative AI model to create video captions. The model is deployed on Vertex AI with autoscaling. During peak hours, they observe high latency and request timeouts. Which action would most effectively address this issue?

Accepted Answer

Increase the minimum number of replicas in the autoscaling configuration. Increasing the minimum number of replicas ensures that during peak hours, the model already has a baseline of warm instances ready to handle requests, reducing cold-start latency and preventing timeouts. Autoscaling can take time to spin up new replicas, so a higher minimum replica count directly mitigates the latency spike by pre-provisioning capacity.

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Optimize the prompt to reduce output length

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Reduce the maximum number of replicas to limit resource usage

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Switch to a GPU-based machine type for faster inference

Question 12

A graphic design company wants to generate high-quality synthetic images for product mockups. Which Google Cloud generative AI service is most suitable?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is the correct choice because it is Google Cloud's state-of-the-art text-to-image diffusion model specifically designed to generate high-quality, photorealistic synthetic images from natural language prompts. This directly meets the requirement for creating product mockups, as Imagen can produce custom visuals with fine-grained control over style and composition, and it integrates seamlessly with Vertex AI for deployment and management.

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AutoML Vision

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Codey APIs for code generation

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Natural Language API

Question 13

A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

Accepted Answer

Implement a retrieval augmented generation (RAG) system that retrieves product facts from a database.. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's output in verifiable, external data sources. By retrieving product facts from a database in real-time, the system ensures that the generated descriptions are based on accurate information, directly mitigating the risk of hallucination. This method combines the generative power of an LLM with a retrieval step that provides factual context, making it ideal for applications where precision is critical.

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Use a code generation model to generate structured descriptions.

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Fine-tune the model on all product descriptions using supervised learning.

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Use a large language model with detailed prompt instructions to be accurate.

Question 14

A company is using Vertex AI to deploy a text generation model for a chatbot. They want to reduce the response latency. Which configuration change is most effective?

Accepted Answer

Use a smaller model variant. Using a smaller model variant directly reduces the number of parameters and computational operations required per inference, which lowers latency. In Vertex AI, smaller models like `text-bison@002` have fewer layers and attention heads than larger counterparts, resulting in faster token generation without requiring hardware changes.

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Enable model quantization

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Increase the number of GPUs

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Use a larger batch size

Question 15

A company is deploying a generative AI model for medical advice. What is the most important consideration?

Accepted Answer

Safety and fairness. In medical advice applications, a generative AI model's outputs can directly impact patient health, making safety and fairness the paramount consideration. Incorrect or biased advice could lead to misdiagnosis or harm, outweighing performance metrics like latency or cost. Regulatory frameworks such as HIPAA and FDA guidelines for clinical decision support further mandate rigorous validation of model safety and fairness before deployment.

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Model latency

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Model size

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Cost of inference