Generative AI Leader Practice Test 27 — 15 Questions

Question 1

A healthcare startup has fine-tuned a Vertex AI PaLM 2 model on a dataset of medical records to generate patient summaries. The model produces fluent text but occasionally fabricates diagnoses not present in the input. The team has already tried increasing the training data size by 20% and adjusting the temperature from 0.7 to 0.2, but hallucinations persist. The summaries must be factually accurate for regulatory compliance. What should the team do next?

Accepted Answer

Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.. Option B is correct because augmenting the model with a retrieval-augmented generation (RAG) pipeline grounded in a trusted medical knowledge base directly addresses hallucination by forcing the model to reference verified sources. Option A is wrong because changing the base model does not solve the fundamental lack of grounding. Option C is wrong because few-shot examples improve output format but not factual accuracy. Option D is wrong because increasing the context window does not prevent fabrication; it may even introduce more irrelevant information.

Answer

Increase the maximum output tokens to allow the model to generate more detailed summaries.

Answer

Add more few-shot examples to the prompt for each generation.

Answer

Switch the base model to Gemini 1.5 Pro without additional changes.

Question 2

A developer is using the Gemini API to generate creative product taglines. The taglines are often bland and uncreative. The developer wants more variety and novelty in the outputs. Which parameter adjustment would most effectively increase the diversity of the generated taglines?

Accepted Answer

Increase temperature from 0.2 to 0.9.. Option A is correct because higher temperature increases the randomness and creativity of the output. Option B is wrong because lower temperature makes output more deterministic and less creative. Option C is wrong because lower top_p reduces diversity. Option D is wrong because a high frequency penalty may discourage novel words, reducing creativity.

Answer

Decrease top_p from 1.0 to 0.5.

Answer

Set frequency_penalty to 2.0.

Answer

Decrease temperature from 0.7 to 0.2.

Question 3

A company is deploying a generative AI system that generates customer-facing emails. The system must ensure outputs are not toxic, biased, or harmful. Which TWO techniques are most effective for reducing toxicity in model outputs without significantly affecting performance?

Accepted Answer

Fine-tune the model on a dataset of safe emails using reinforcement learning from human feedback (RLHF).. Options A and D are correct. Fine-tuning with RLHF (or using a safety-tuned model) directly aligns the model to avoid toxic outputs. Output filtering (e.g., safety classifiers) provides a robust post-processing layer. Option B (temperature) does not prevent toxicity, only randomness. Option C (few-shot) is insufficient for safety. Option E (increasing tokens) may increase risk.

Answer

Increase the maximum output token count to allow more context.

Answer

Set temperature to a very low value (e.g., 0.1).

Answer

Provide 50 few-shot examples of safe emails in every prompt.

Question 4

A financial institution is implementing a generative AI chatbot to handle customer inquiries. The institution must comply with regulatory requirements (e.g., GDPR, SOX) and ensure data privacy. Which TWO actions should the institution take?

Accepted Answer

Establish a Center of Excellence (CoE) for AI governance to oversee model deployment and monitoring.. Options B and D are correct. B: implementing model validation and testing ensures the model behaves as expected and helps meet compliance requirements. D: establishing a Center of Excellence (CoE) for AI governance provides oversight and standardization. Option A is wrong because using a pre-trained model without customization may not meet specific compliance needs. Option C is wrong because deploying on-premises only is not necessary and may limit scalability. Option E is wrong because Vertex AI without data governance would not satisfy regulatory demands.

Answer

Use Vertex AI without additional data governance controls to simplify deployment.

Answer

Use a pre-trained model without customization to reduce development time.

Answer

Deploy the model on-premises only to keep data within local infrastructure.

Question 5

A healthcare company is using Vertex AI to build a generative AI assistant that helps doctors draft clinical notes. The assistant uses a fine-tuned PaLM 2 model deployed on a private endpoint. Recently, doctors have reported that the assistant takes over 30 seconds to respond, causing workflow delays. Additionally, the monthly Vertex AI costs have increased by 40% without a proportional increase in usage. The model responses are generally accurate but sometimes include irrelevant details. The company wants to improve response time and cost while maintaining acceptable quality. A review of logs shows that most requests are for similar note types (e.g., progress notes, discharge summaries) and that the same prompt is used repeatedly with minor variations. What should the company do first?

Accepted Answer

Implement response caching for common queries and batch process similar requests. Option B is correct because implementing caching and batching directly addresses latency and cost by reusing common responses and processing requests in groups. Option A (switching to a larger model) would increase latency and cost. Option C (increasing quota) does not improve performance or cost efficiency. Option D (model quantization) might help latency but could reduce accuracy; it's also more complex than caching/batching as a first step.

Answer

Switch to a larger model (e.g., Gemini 1.5 Pro) to improve response quality and reduce irrelevant details

Answer

Increase the Vertex AI endpoint's maximum request quota to handle concurrent requests

Answer

Apply model quantization (e.g., INT8) to reduce model size and inference time

Question 6

A company deployed a large language model on Vertex AI using the configuration shown in the exhibit. During peak usage, users report high latency. Which change is most likely to improve latency?

Accepted Answer

Increase minReplicaCount to 3.. Option A is correct because increasing the minimum number of replicas ensures that more instances are ready to serve traffic during bursts, reducing the time spent scaling up. Option B is wrong because the GPU (T4) is already suitable for inference; upgrading may not address the core latency issue. Option C is wrong because switching to a less powerful machine type (n1-standard-4) would likely increase latency. Option D is wrong because removing the accelerator would significantly degrade performance for a large model.

Answer

Remove the accelerator to simplify deployment.

Answer

Switch to a GPU with more memory, such as NVIDIA_TESLA_A100.

Answer

Change machineType to n1-standard-4 to reduce cost.

Question 7

A large enterprise has deployed generative AI assistants in three separate departments (HR, Marketing, and Customer Support) using different tools and models. Over the past quarter, the company has observed escalating cloud costs, inconsistent user experiences, and reports of data leakage in Customer Support logs. The CTO wants to address these issues while maintaining innovation velocity. As the Generative AI Leader, what course of action should you recommend?

Accepted Answer

Implement a centralized AI governance platform with cost monitoring, model registry, and security guardrails.. Option C is correct because implementing a centralized AI governance platform provides unified cost management, security controls, and standardization, aligning with business strategies to scale generative AI responsibly. Option A is wrong because creating additional silos would worsen fragmentation. Option B is wrong because it fails to address root causes and misses an opportunity for optimization. Option D is wrong because regulatory compliance is not optional and ignoring it can lead to severe penalties.

Answer

Standardize on a single model and tool across all departments, restricting usage to one platform.

Answer

Discontinue the Customer Support assistant to eliminate data leakage risk and reduce costs.

Answer

Allow each department to continue independently but require monthly cost and compliance reports.

Question 8

A retail company wants to use generative AI to generate product descriptions for thousands of items. They need to ensure that the descriptions are consistent with their brand voice and do not contain factual inaccuracies. What is the most effective strategy?

Accepted Answer

Fine-tune a model on historical product descriptions and use prompt engineering with brand guidelines.. Option B is correct because fine-tuning on historical product descriptions tailors the model to the brand's specific style, and prompt engineering with brand guidelines ensures adherence to voice and reduces hallucinations. Option A is wrong because a generic pre-trained model may not capture brand voice and is more prone to hallucination. Option C is wrong because rule-based systems lack the flexibility and creativity of generative AI. Option D is wrong because removing safety filters increases the risk of inappropriate or inaccurate content.

Answer

Use a rule-based system to generate descriptions from product attributes.

Answer

Use a large language model with no safety filters to maximize output variety.

Answer

Use a pre-trained model without any customization and rely on post-processing filters.

Question 9

A retail company uses the Vertex AI Gemini API to generate product descriptions. Recently, the model started producing factually incorrect statements about product specifications, such as wrong dimensions and materials. Which strategy should be implemented to improve factual accuracy?

Accepted Answer

Use grounding with Vertex AI Search to retrieve verified product data. Option B is correct because grounding with Vertex AI Search retrieves authoritative information from the company's knowledge base, reducing hallucinations. Option A (increasing temperature) would increase randomness, worsening accuracy. Option C (fine-tuning on product images) does not address factual text inaccuracies. Option D (enabling model versioning) helps with version control but not with correctness of responses.

Answer

Enable model versioning to automatically roll back to a previous version

Answer

Fine-tune the model on a dataset of product images and descriptions

Answer

Increase the temperature parameter to 0.9

Question 10

A company is deploying a large language model (LLM) for customer support using Vertex AI. Which TWO best practices should they follow to ensure high-quality and cost-effective responses?

Accepted Answer

Implement prompt optimization techniques to tailor responses. The correct answers are B (implement prompt optimization) and D (use Vertex AI Model Monitoring for drift). Prompt optimization improves response quality, and model monitoring detects performance degradation. Option A is wrong because using a single large model for all queries may be inefficient; smaller specialized models or routing can be better. Option C is risky for production workloads. Option E exposes sensitive prompts.

Answer

Deploy the model on Spot VMs to reduce infrastructure costs

Answer

Store prompts in plain text files for easy version control

Answer

Use a single large model for all query types to maintain consistency

Question 11

A developer runs the command above to test a text classification model deployed on a Vertex AI endpoint. The model returns an error. What is the most likely cause?

Accepted Answer

The instance schema (e.g., 'content' field) does not match the model's expected input signature. Option C is correct because the error 'Model does not support the given instance format' indicates a mismatch between the input schema and what the model expects. Option A (wrong endpoint ID) would produce a 'not found' error. Option B (region mismatch) would give a regional validation error. Option D (model not deployed) would result in an endpoint not serving error.

Answer

The endpoint ID '789' does not exist in the project

Answer

The model is not deployed to any endpoint

Answer

The region 'us-central1' does not match the region where the model is deployed

Question 12

A company needs to fine-tune a foundation model on Vertex AI for a custom text classification task with only 500 labeled examples. They want to minimize cost while achieving high accuracy. What is the MOST cost-effective approach?

Accepted Answer

Design prompts with few-shot examples and test it with the available data.. Option B is correct because with a small dataset, prompt engineering and few-shot examples are often sufficient and much cheaper than fine-tuning. Fine-tuning (C) risks overfitting and higher cost. Model distillation (D) requires a large teacher model. Evaluation (A) is not a training method.

Answer

Fine-tune the foundation model using full fine-tuning on the entire dataset.

Answer

Use model distillation to train a smaller student model.

Answer

Use Vertex AI LLM-based evaluation to compare multiple large models and select the best one.

Question 13

Which THREE capabilities are provided by Vertex AI Agent Builder? (Choose three.)

Accepted Answer

Integration with Dialogflow CX for conversational flows.. Options A, B, and D are correct. Vertex AI Agent Builder allows creating custom agents with memory and tools (A), integrates with Dialogflow CX (B), and provides built-in grounding with Google Search (D). Automated hyperparameter tuning (C) is a feature of Vertex AI Training, not Agent Builder. Multimodal inputs (E) are supported by Gemini models but not a built-in capability of Agent Builder.

Answer

Automated model hyperparameter tuning.

Answer

Support for multimodal (text, image, video) input processing in agents.

Question 14

Which command correctly updates the traffic split?

Accepted Answer

gcloud ai endpoints update-traffic-split my-endpoint --region=us-central1 --traffic-split=model-v2=20,model-v1=80. Option D is correct because the command `gcloud ai endpoints update-traffic-split` with the `--traffic-split` flag is the proper way to modify traffic percentages for an endpoint. Other commands are either incorrect subcommands or do not exist.

Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --remove-deployed-model model-v1 --add-deployed-model model-v2 --traffic-split=20

Answer

gcloud ai models update sentiment-model-v2 --traffic-split=20

Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --update-traffic-split=model-v2=20,model-v1=80

Question 15

A company uses a text generation model for customer support but notices it occasionally provides outdated information. Which technique should they implement to improve output accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG). Option B is correct because RAG (Retrieval-Augmented Generation) retrieves current information from a knowledge base, ensuring factual accuracy. Option A (temperature increase) would increase randomness, making outputs less reliable. Option C (fine-tuning with historical data) might not include recent updates. Option D (max tokens) only affects length, not accuracy.

Answer

Increase max output tokens

Answer

Fine-tune the model with more historical support data

Answer

Increase model temperature to 1.0