Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 7 — 15 Questions

Question 1

A company wants to build a chatbot that can answer questions about its internal knowledge base using natural language. Which Google Cloud Generative AI offering should they use to quickly prototype and deploy this chatbot with minimal coding?

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

Accepted Answer

Generative AI Studio. Generative AI Studio provides a no-code/low-code environment to prototype and deploy chatbots with foundation models.

Answer

Vertex AI Endpoints

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Cloud Natural Language API

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Vertex AI Model Garden

Question 2

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

Accepted Answer

safety_settings. Safety settings in the Vertex AI PaLM API allow you to configure thresholds for filtering harmful content categories (e.g., toxicity, harassment, hate speech). By adjusting these settings, you can block or reduce the likelihood of toxic outputs before they are returned, directly addressing the problem without altering the model's creativity or randomness.

Answer

max_output_tokens

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temperature

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top_k

Question 3

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

Accepted Answer

Perform supervised fine-tuning using their labeled dataset. Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.

Answer

Deploy the model with a larger max_output_tokens

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Use prompt engineering with few-shot examples

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Increase the temperature to 0.9

Question 4

A developer is using the Vertex AI PaLM API and receives a 429 Resource Exhausted error. What is the most likely cause?

Accepted Answer

The user has exceeded the allowed number of requests per minute. 429 errors indicate rate limiting or quota exhaustion for the API.

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The request payload is too large

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The model is not available in the current region

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The API key is invalid

Question 5

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

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Pickle serialized objects

Question 6

A company is using Vertex AI Model Registry to manage multiple versions of its custom generative model. They want to automatically route a percentage of traffic to a new model version for testing. What should they do?

Accepted Answer

Deploy both versions to the same endpoint and adjust traffic split settings. Vertex AI Endpoints support traffic splitting between model versions.

Answer

Set up a Cloud Tasks queue to distribute requests

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Create a new endpoint for each version

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Use a load balancer in front of the endpoints

Question 7

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

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ROUGE-L

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BLEU

Question 8

A developer needs to use the Vertex AI PaLM API to generate text embeddings for a large corpus of documents. Which model should they use?

Accepted Answer

textembedding-gecko@001. `textembedding-gecko@001` is the specific Vertex AI model designed for generating text embeddings, which convert text into dense vector representations. This model is optimized for semantic similarity, clustering, and retrieval tasks, making it ideal for processing a large corpus of documents. The other models are designed for code generation, text generation, or chat, not embeddings.

Answer

codey-bison@001

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text-bison@001

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chat-bison@001

Question 9

A machine learning engineer is deploying a large generative model on Vertex AI. The model requires a GPU with high memory. Which machine configuration should they choose?

Accepted Answer

a2-highgpu-4g with 4 A100 GPUs. The a2-highgpu-4g machine series is specifically designed for large-scale GPU-accelerated workloads, offering 4 NVIDIA A100 GPUs with 40GB of high-bandwidth memory (HBM2e) each, totaling 160GB of GPU memory. This configuration provides the high memory capacity required for training or serving large generative models, such as LLMs or diffusion models, which often exceed the memory limits of smaller GPUs.

Answer

c2-standard-16 with no GPU

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n1-standard-4 with a single T4 GPU

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n2-standard-8 with a single P4 GPU

Question 10

An organization is building a search application using Vertex AI Vector Search. They have encoded their documents into embeddings and want to retrieve the most similar documents for a query. Which TWO actions are required to set up a Vector Search index?

Accepted Answer

Deploy the index to the IndexEndpoint.. Deploying the index to an IndexEndpoint is a required step to make the Vector Search index available for online queries. Without deployment, the index exists only as a metadata resource and cannot serve similarity search requests. The deployment binds the index to a specific endpoint, allocating machine resources for real-time inference.

Answer

Specify embedding dimension size in the index config.

Answer

Train a custom embedding model.

Answer

Download the query embeddings to local storage.

Question 11

A developer is using the Vertex AI PaLM API to generate code. They want to ensure the output is safe and adheres to company policies. Which THREE attributes can they configure in the safety_settings parameter?

Accepted Answer

Toxicity. The safety_settings parameter in the Vertex AI PaLM API allows developers to filter content based on predefined harm categories, including toxicity. This setting enables the API to block or adjust responses that contain toxic language, ensuring the generated code adheres to company safety policies by preventing harmful or offensive outputs.

Answer

Language detection

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Sentiment analysis

Question 12

The exhibit shows the output of describing a model on Vertex AI. What does 'modelSource: MODEL_GARDEN' indicate about this model?

Accepted Answer

The model was imported from the Vertex AI Model Garden.. 'modelSource: MODEL_GARDEN' explicitly indicates that the model was sourced from Vertex AI Model Garden, which is a curated repository of pre-built and pre-trained foundation models. This field is set when a model is imported from Model Garden, not when it is trained or fine-tuned from scratch within Vertex AI.

Answer

The model was trained on Vertex AI from scratch.

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The model has been exported to Model Garden.

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The model was fine-tuned using AutoML.

Question 13

The exhibit shows a command to deploy a model to a Vertex AI endpoint with GPU. The deployment fails due to a resource constraint. What is the most likely reason?

Accepted Answer

The machine type n1-standard-4 does not support GPU accelerators.. The n1-standard-4 machine type does not support attaching GPUs. In Vertex AI, GPU accelerators require specific machine series (e.g., n1-highmem-* or n1-highcpu-* for NVIDIA Tesla GPUs, or newer machine families like a2-highgpu-* for A100 GPUs). The n1-standard-4 is a general-purpose machine type that lacks the necessary PCIe lanes and power delivery to accommodate a GPU accelerator, causing a resource constraint failure during deployment.

Answer

The --model flag points to an autoML model.

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The accelerator type is misspelled.

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The min-replica-count is greater than the max-replica-count.

Question 14

A startup wants to quickly integrate a generative AI chatbot into their customer support platform. They need a solution that can answer questions based on their internal knowledge base with minimal setup. Which Google Cloud service should they use?

Accepted Answer

Use Vertex AI Agent Builder to create a conversational agent grounded in their data. Vertex AI Agent Builder (now part of Vertex AI Agent Platform) provides a low-code, out-of-the-box solution for building conversational agents that are grounded in enterprise data. It automatically handles retrieval-augmented generation (RAG) by indexing the startup's internal knowledge base into a vector store and orchestrating the LLM to answer questions using only that data, requiring minimal setup compared to manual integration or fine-tuning.

Answer

Use Model Garden to deploy a pre-built Q&A model

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Call the Gemini API directly and implement grounding logic manually

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Use Cloud AI Notebooks to fine-tune a model on their knowledge base

Question 15

A large enterprise is migrating their on-premise ML workloads to Vertex AI. They have a custom PyTorch model for text classification that they want to serve with minimal code changes. Which Vertex AI capability should they use for model serving?

Accepted Answer

Vertex AI Prediction with a custom container. Vertex AI Prediction with a custom container allows the enterprise to package their existing PyTorch model with any custom dependencies or runtime configurations into a Docker container, enabling deployment with minimal code changes. This approach provides full control over the serving environment while leveraging Vertex AI's managed infrastructure for scaling, monitoring, and endpoint management.

Answer

Vertex AI Endpoints with a pre-built PyTorch runtime

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Vertex AI Model Garden

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Vertex AI Vector Search for approximate nearest neighbor