Generative AI Leader Practice Test 7 — 10 Questions

Question 1

A company is evaluating Google Cloud's generative AI offerings for enterprise use. Which TWO considerations are most important when selecting the right model deployment option?

Accepted Answer

Data residency. Data residency (A) is a critical consideration because enterprise deployments must comply with regional and regulatory requirements, and Google Cloud's Vertex AI lets you pin model endpoints and data processing to specific locations such as us-central1 or europe-west4 to satisfy sovereignty and compliance mandates. Latency requirements (D) are equally important because the deployment option determines network proximity and serving characteristics — for example, a regional endpoint close to users or a provisioned throughput configuration reduces round-trip time, which directly affects real-time generative AI application performance. Together, residency and latency drive the choice between options like global vs. regional endpoints, on-demand vs. provisioned throughput, and Vertex AI vs. Gemini API. Developer preference (B) is subjective and does not determine the correct deployment architecture for enterprise compliance or performance needs. Model size (C) is a model characteristic rather than a deployment-selection criterion, since Vertex AI abstracts the underlying infrastructure. Training time (E) is irrelevant to deployment selection because it concerns the model-building phase, not how the model is served in production.

Answer

Developer preference

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Model size

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Training time

Question 2

A machine learning engineer is building a text-to-image model using Vertex AI. They want to reduce inference latency. Which strategy 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 pass, which lowers latency. In text-to-image models like Imagen or Stable Diffusion, the model size is the primary driver of forward-pass time, so a smaller variant (e.g., fewer layers or reduced latent dimensions) yields faster generation.

Answer

Use a larger image resolution

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Enable batch processing

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Increase the number of inference steps

Question 3

A developer is building a multimodal application that needs to analyze images, understand spoken language, and generate text responses. Which Google Cloud generative AI model is BEST suited for this task?

Accepted Answer

Gemini 1.5 Pro. Gemini models are natively multimodal, accepting text, image, audio, and video inputs. PaLM 2 is text-only. Imagen is for image generation. Chirp is for speech recognition/synthesis.

Answer

PaLM 2 for text generation

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Imagen for image generation

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Chirp for speech recognition

Question 4

During a proof-of-concept for a GenAI document summarization tool, the team wants to evaluate whether the summaries are accurate and retain key information before scaling. Which evaluation approach is most appropriate for this stage?

Accepted Answer

Run an A/B test with a small user group and have domain experts manually review a sample of summaries for accuracy. A/B testing with manual review by domain experts provides qualitative and quantitative feedback on accuracy and completeness, which is crucial for a pilot. Automated metrics alone may not capture business relevance.

Answer

Measure latency and cost as the primary evaluation metrics

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Deploy to all users and collect feedback via a survey

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Use ROUGE scores exclusively to compare summaries against human-written ones

Question 5

Which THREE are best practices for designing prompts for a generative AI model?

Accepted Answer

Provide few-shot examples for complex tasks. Providing few-shot examples (e.g., 2-5 input-output pairs) helps the model infer the desired pattern, reducing ambiguity for complex tasks like classification or structured extraction. This technique leverages in-context learning, where the model uses the examples as a template without fine-tuning.

Answer

Use negative prompts to avoid undesired outputs

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Always set temperature to 1.0 for creativity

Question 6

A company uses Vertex AI to deploy a generative model for customer support. They need to ensure that the model does not produce harmful content when responding to user queries. Which of the following is the MOST effective approach?

Accepted Answer

Use Vertex AI's safety filters and adjust safety settings per category. Vertex AI's safety filters and adjustable safety settings are the built-in, most effective way to block harmful content. The other options are either insufficient or place undue burden on users.

Answer

Instruct customers to report harmful responses for future model updates

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Prompt the model to 'be safe' and rely on its training to avoid harmful content

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Review all responses manually before sending to customers

Question 7

An ML engineer sees the above deployment output. The business wants to reduce inference cost. Which action should they take?

Accepted Answer

Change to a lower-cost machine type. Switching to a lower-cost machine type directly reduces the per-request compute cost without altering the model architecture or inference logic. This is a common cost-optimization strategy in cloud-based ML deployments, where instance types (e.g., from GPU to CPU or from a larger to a smaller GPU) can be selected based on latency and throughput requirements, provided the model fits within the machine's memory and compute constraints.

Answer

Use a larger model

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Deploy to multiple regions

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Increase traffic split

Question 8

A manufacturing company wants to use generative AI to create maintenance manuals from sensor data. The manuals must be accurate and reflect the latest equipment configurations. Which approach best ensures data freshness and consistency?

Accepted Answer

Use a retrieval-augmented generation (RAG) system that queries a live database of sensor configurations.. A retrieval-augmented generation (RAG) system retrieves the most current equipment configurations directly from a live database at inference time, ensuring the generated manual reflects real-time sensor data without requiring model retraining. This approach decouples the static knowledge in the LLM from the dynamic data source, guaranteeing both accuracy and freshness while avoiding the latency and cost of continuous retraining.

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Train the model in real-time as sensor data streams in.

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Periodically retrain the model with the latest sensor data.

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Have human technicians review and update the manuals manually.

Question 9

A financial services firm is using a foundation model on Vertex AI to generate investment summaries from quarterly reports. The summaries are accurate but often miss key financial metrics and trends. The team cannot afford to fine-tune the model frequently. Which technique should they use to improve the completeness and relevance of the summaries without modifying the model?

Accepted Answer

Provide three few-shot examples in the prompt that highlight the desired metrics.. Few-shot prompting provides the model with concrete examples of desired output structure and content, guiding it to include key financial metrics and trends without retraining. This technique leverages in-context learning, where the model generalizes from the examples in the prompt to produce more complete and relevant summaries, while avoiding the cost and latency of fine-tuning.

Answer

Increase temperature to 0.9 to encourage more creative outputs.

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Set stop sequences to ['

'] to ensure the model finishes each paragraph.

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Lower top_p to 0.5 to reduce the sampling pool.

Question 10

A developer is using the Vertex AI Gemini API to generate product descriptions. They get a 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192.' What is the most likely issue?

Accepted Answer

The prompt is too long. The 400 error 'INVALID_ARGUMENT: The model's maximum input token limit is 8192' explicitly indicates that the combined token count of the prompt (system instructions, user input, and any conversation history) exceeds the 8192-token context window of the Gemini model being used. This is a hard limit enforced by the Vertex AI Gemini API, and the error is triggered before any generation begins. Therefore, the most likely issue is that the prompt is too long.

Answer

The API key is invalid

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The output tokens are too high

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The model is not available in the region