Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 5 — 15 Questions

Question 1

Why is the model responding in English despite the prompt asking for French translation?

Accepted Answer

The system instruction to translate to French was not set; the user prompt alone is not sufficient. In Google Cloud's Vertex AI and Generative AI offerings, the system instruction is a separate, persistent directive that sets the model's behavior, such as language output. The user prompt alone, even if it asks for a French translation, is not sufficient to override the default language of the model; the system instruction must explicitly specify the target language. Without this instruction, the model defaults to its training language (typically English), regardless of the user's request.

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The model endpoint is configured for English only

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The temperature is too high, causing random outputs

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The maxOutputTokens is too low to complete the translation

Question 2

A company is building a generative AI chatbot for customer support using Vertex AI. They want to ground the model responses with their internal knowledge base stored in Cloud Storage and BigQuery. Which feature should they use to ensure the model only answers from the provided data and avoids hallucination?

Accepted Answer

Vertex AI Grounding with Vertex AI Search. Vertex AI Grounding with Vertex AI Search is the correct feature because it allows the model to retrieve and cite information from a specified data source (such as Cloud Storage and BigQuery) to generate responses. This process, known as grounding, ensures the model's output is based solely on the provided authoritative data, effectively reducing hallucinations by constraining the model to factual, retrieved content rather than relying on its internal parametric knowledge.

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Vertex AI Prediction

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Vertex AI Pipelines

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Cloud Functions

Question 3

A financial services firm needs to deploy a large language model (LLM) for analyzing sensitive client documents. They require the model to run within their Virtual Private Cloud (VPC) with no internet access and must comply with data residency regulations. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Model Garden with private endpoints and VPC Service Controls. Vertex AI Model Garden with private endpoints and VPC Service Controls allows the LLM to be deployed entirely within the customer's VPC, with no internet egress, and enforces data residency by restricting data movement to the configured VPC boundary. Private endpoints use Private Service Connect to route inference traffic through internal IPs, while VPC Service Controls prevent data exfiltration and ensure compliance with residency regulations.

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Vertex AI Search

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Cloud Run

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Vertex AI Workbench

Question 4

A developer is using Vertex AI Studio to prototype a chat application. They want to provide the model with a system instruction to set the tone and style. How should they configure this in the Vertex AI Studio interface?

Accepted Answer

Use the 'System Instruction' field in the model configuration. Vertex AI Studio provides a dedicated 'System Instruction' field in the model configuration panel, which allows developers to set the tone, style, and behavioral guidelines for the model without mixing them into the user prompt. This field is specifically designed to hold system-level instructions that are prepended to the conversation context, ensuring consistent behavior across multiple turns.

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Add the instruction as part of the prompt text

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Set the temperature parameter to a high value

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Add the instruction in the 'Context' parameter

Question 5

A company is using Vertex AI for multimodal generative AI to analyze images and text. They need to ensure that the model's outputs are auditable and can be traced back to the input data. Which feature should they enable?

Accepted Answer

Vertex AI Model Monitoring with Explainable AI. Vertex AI Model Monitoring with Explainable AI provides feature attributions that map model predictions back to specific input features (e.g., pixels in images or tokens in text). This creates an auditable trail by quantifying how each input contributed to the output, enabling traceability for compliance and debugging. The other options lack the direct input-to-output attribution required for auditability.

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Vertex AI Feature Store

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Vertex AI Experiments

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Cloud Logging

Question 6

A data scientist wants to fine-tune a foundation model from Vertex AI Model Garden on their custom dataset. They want to choose a cost-effective method that updates only a small subset of parameters. Which fine-tuning approach should they use?

Accepted Answer

Parameter-Efficient Fine-Tuning (PEFT) like LoRA. Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are specifically designed to update only a small subset of parameters (e.g., low-rank matrices injected into transformer layers) while keeping the majority of the foundation model frozen. This drastically reduces memory and compute costs compared to full fine-tuning, making it the most cost-effective choice for customizing a model from Vertex AI Model Garden on a custom dataset.

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Full fine-tuning

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Prompt tuning

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Reinforcement Learning from Human Feedback (RLHF)

Question 7

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 8

Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sensitive application? (Choose THREE.)

Accepted Answer

Model size and architecture. Model size and architecture directly impact inference latency because larger models with more parameters require more computation per request. For latency-sensitive applications, choosing a smaller or distilled model (e.g., Gemma 2B vs. 27B) or using quantization can reduce response times. Vertex AI Endpoints serve the model as-is, so the model's inherent computational cost is the primary driver of per-request latency.

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Number of model versions

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Number of model instances

Question 9

A developer receives the above JSON response from a Vertex AI language model. The output content is correct, but the developer expected the model to not answer geography questions. What should the developer do to prevent the model from responding to geography queries?

Accepted Answer

Add a system instruction to not answer geography questions. Vertex AI does not have a predefined safety filter category for 'Geography'. To prevent the model from answering geography questions, the developer should use a system instruction that explicitly tells the model not to respond to geography queries. System instructions are the appropriate mechanism for custom content restrictions, while safety filters are limited to predefined categories such as toxic, harassment, etc.

Answer

Adjust the safety filter thresholds for the 'Toxic' category

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Enable Vertex AI Grounding with a geography knowledge base

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Configure a safety filter for the 'Geography' category

Question 10

A team deployed a custom generative AI model using KServe on Google Kubernetes Engine (GKE) with the above configuration. They notice that the model is taking longer than expected to respond. What is the most likely cause?

Accepted Answer

The model requires more than 1 GPU for acceptable performance. The configuration specifies 1 GPU, but the model requires more than 1 GPU for acceptable performance. KServe on GKE allocates GPU resources based on the `limits` field; if the model's inference workload exceeds the memory bandwidth or compute capacity of a single GPU, latency increases due to queuing and serialization. This is the most likely cause of the slow response time, as GPU-bound models are sensitive to under-provisioning.

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The CPU resource limits are too low

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The model is crashing due to insufficient memory

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The container image is too large and takes time to pull

Question 11

You are a machine learning engineer at a healthcare startup. Your team has developed a generative AI model that summarizes patient medical records. The model is deployed on Vertex AI Endpoints using a custom container. You have configured the endpoint with a single n1-standard-4 machine (4 vCPUs, 15 GB memory) without accelerators. The model uses a small transformer architecture. During load testing with 50 concurrent requests, you observe that the average latency is 8 seconds, which exceeds the requirement of 2 seconds. Additionally, some requests time out after 10 seconds. You suspect the CPU is the bottleneck. You also notice that the model inference code uses TensorFlow but is not optimized for inference. Which action should you take to reduce latency?

Accepted Answer

Deploy the model on a machine with a GPU and use TensorRT for inference optimization.. The CPU is identified as the bottleneck, and deploying on a GPU with TensorRT optimization directly addresses this by accelerating the TensorFlow inference. TensorRT optimizes the model graph and fuses layers, significantly reducing latency for transformer-based models, which is essential to meet the 2-second requirement.

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Reduce the model size by pruning and quantization, then redeploy.

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Increase the request timeout to 30 seconds to accommodate the latency.

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Enable autoscaling to handle the load with multiple replicas.

Question 12

You are a generative AI architect for a large e-commerce company. Your team has built a product description generator using Vertex AI's text-bison model. The model is accessed via the Vertex AI API from a web application. You have set the temperature to 0.5 and top_k to 40. The team reports that the generated descriptions are often too generic and lack creativity. They want the descriptions to be more diverse and engaging. You are also concerned about cost, as each API call is billed. Which change should you recommend to increase creativity while managing cost?

Accepted Answer

Increase the temperature to 0.8 and keep top_k at 40.. Increasing the temperature to 0.8 makes the model's output probability distribution flatter, which increases randomness and allows less likely tokens to be selected. This directly addresses the need for more diverse and creative descriptions. Keeping top_k at 40 ensures the model still considers a broad set of candidate tokens, balancing creativity with coherence, and does not increase API call costs since temperature and top_k are inference parameters that do not affect billing.

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Keep temperature at 0.5 but reduce top_k to 20.

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Switch to a larger model like text-bison@002 and keep same parameters.

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Decrease the temperature to 0.2 and increase top_k to 60.

Question 13

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.

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Grounding with Google Search

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Model tuning

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Prompt engineering

Question 14

A data scientist is using Vertex AI Model-as-a-Service (MaaS) to deploy a fine-tuned open-source model. They notice high latency during inference. What is the most likely cause?

Accepted Answer

The endpoint is set to autoscaling with a low minimum node count. High latency during inference on Vertex AI MaaS is most often caused by the endpoint scaling configuration. When autoscaling is enabled with a low minimum node count, the system may need to provision additional nodes to handle the request load, which introduces cold-start latency. This is especially pronounced for fine-tuned open-source models, which can be large and take time to load onto new nodes.

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The model is too large for the hardware

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The model is not quantized

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The region is incorrect

Question 15

You want to use a Google foundation model to generate text summaries of news articles. Which Vertex AI service should you use?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio (now part of Vertex AI Agent Builder) provides a no-code/low-code environment to access, test, and tune Google's foundation models, including PaLM 2 and Gemini, specifically for generative tasks like text summarization. It offers built-in prompt templates and safety settings tailored for summarization use cases, making it the correct service for this task.

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Vertex AI Prediction

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Vertex AI Model Registry

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Vertex AI Feature Store