Generative AI Leader Practice Test 40 — 15 Questions

Question 1

A team has developed a generative AI model for real-time translation. The evaluation metrics and business requirements are shown. Which business decision is most appropriate given the trade-offs?

Accepted Answer

Optimize the model for cost efficiency, even if accuracy drops slightly to 90%.. The business requirements prioritize cost efficiency as the primary constraint, and the model currently exceeds the cost target. A slight accuracy drop to 90% (still within acceptable limits) allows cost to be reduced, aligning with the core business goal. The trade-off is acceptable since latency and other metrics remain within bounds, and accuracy at 90% still meets the minimum threshold for real-time translation quality.

Answer

Accept the model as-is because all other metrics are within limits.

Answer

Prioritize latency reduction even if it increases cost.

Answer

Reduce accuracy to 85% to achieve both latency and cost targets.

Question 2

A healthcare company wants to use Gemini to analyze patient records and summarize findings. Which data privacy practice is most critical when using the Gemini API on Vertex AI?

Accepted Answer

Enable Vertex AI Data Governance to mask or redact PII before sending to the API.. Vertex AI Data Governance allows you to configure data masking or redaction of personally identifiable information (PII) before the data is sent to the Gemini API, ensuring compliance with healthcare regulations like HIPAA. This is the most critical practice because it prevents PHI from being exposed to the model or stored in logs, directly addressing the core privacy requirement. Disabling logging alone (Option B) does not prevent PHI from being processed by the model, and fine-tuning with PHI (Option A) introduces significant compliance risks.

Answer

Fine-tune Gemini using PHI to improve accuracy.

Answer

Disable request-response logging in Vertex AI to ensure data is not stored.

Answer

Use the text-davinci-003 model instead of Gemini, as it is more private.

Question 3

A healthcare provider is planning to deploy generative AI for clinical note summarization. Which THREE actions are essential for regulatory compliance (e.g., HIPAA)?

Accepted Answer

Implement role-based access controls to limit who can view AI-generated notes.. Role-based access controls (RBAC) are a core requirement under HIPAA's Security Rule (45 CFR § 164.312(a)(1)) to ensure that only authorized personnel can access electronic protected health information (ePHI). In the context of generative AI for clinical note summarization, RBAC prevents unauthorized viewing of AI-generated summaries that may contain sensitive patient data, thereby enforcing the minimum necessary standard.

Answer

Allow clinicians to share AI-generated summaries with anyone in the organization.

Answer

Store raw patient data in model training logs for auditing.

Question 4

A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?

Accepted Answer

Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.. Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This method uses the underlying grounding service to query a vector-based data store (powered by Vertex AI Search) and append source references to the model's output, ensuring factual accuracy without retraining.

Answer

Use the Codey API for text generation.

Answer

Use the base model without any grounding to maximize flexibility.

Answer

Fine-tune the model on the documentation and deploy.

Question 5

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.

Answer

Model latency

Answer

Model size

Answer

Cost of inference

Question 6

A model generates responses that frequently repeat phrases or words. Which parameter adjustment is most likely to fix this?

Accepted Answer

Increase repetition penalty. Increasing the repetition penalty directly discourages the model from selecting tokens that have already appeared in the generated sequence, thereby reducing repetitive phrases or words. This parameter works by subtracting a fixed penalty from the logits of previously generated tokens before applying the softmax function, making them less likely to be chosen again.

Answer

Increase top_k

Answer

Increase temperature

Answer

Increase max output tokens

Question 7

You are a generative AI architect at a social media company. You are tasked with building a content moderation system that uses a generative model to flag toxic comments. The system must have very low false positive rates (i.e., not flag harmless comments) to avoid user backlash, but it must catch nearly all toxic comments. You have a large dataset of labeled toxic and non-toxic comments. You plan to use a pre-trained LLM and fine-tune it for classification. During experimentation, you notice that the model's recall for toxic comments is high (95%) but its precision is low (60%), leading to many false positives. You need to improve precision without substantially reducing recall. Which approach should you try first?

Accepted Answer

Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic).. Tuning the classification threshold to a higher value directly addresses the low precision (high false positive rate) by requiring a higher confidence level before labeling a comment as toxic. This reduces false positives while maintaining high recall, as the model's underlying learned representations remain unchanged. The threshold adjustment is a standard post-hoc calibration technique that trades off precision and recall without retraining or altering the model architecture.

Answer

Gather additional toxic comments from similar platforms to augment the training data.

Answer

Apply a higher weight to the toxic class in the loss function during fine-tuning.

Answer

Use a smaller pre-trained model that is inherently less sensitive to subtle toxic language.

Question 8

A developer is using Vertex AI PaLM 2 to generate product descriptions. The output is often too verbose and includes irrelevant details. Which technique should the developer apply?

Accepted Answer

Use few-shot prompting with examples of concise descriptions. The developer needs to constrain the model's output to be concise and relevant. Few-shot prompting provides the model with explicit examples of the desired output format (concise descriptions), guiding it to mimic that style and length. This directly addresses verbosity and irrelevant details without altering the model's fundamental randomness or safety settings.

Answer

Set top_p to 0.1

Answer

Enable safety filters

Answer

Increase temperature to 0.9

Question 9

Refer to the exhibit. A developer sees this error when trying to call a Vertex AI endpoint for online prediction. What permission does the requesting identity need to be granted?

Accepted Answer

aiplatform.endpoints.predict. The error occurs when calling a Vertex AI endpoint for online prediction, which requires the `aiplatform.endpoints.predict` permission. This permission is specifically scoped to the endpoint resource, allowing the identity to send prediction requests to a deployed model endpoint. The correct IAM role binding must include this permission for the requesting identity to successfully invoke the endpoint.

Answer

aiplatform.prediction.predict

Answer

aiplatform.endpoints.use

Answer

aiplatform.models.predict

Question 10

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. The logs show that most requests are for similar note types with repeated prompts, making response caching ideal for reducing latency and cost. Caching stores responses for identical or near-identical queries, eliminating redundant inference calls, which directly addresses the 30-second response time and 40% cost increase without sacrificing quality.

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 11

A data analyst wants to build a regression model in BigQuery to predict sales from historical data without writing any Python code. Which BigQuery ML statement should they use to define the model?

Accepted Answer

CREATE MODEL my_model OPTIONS(model_type='linear_reg') AS SELECT .... The CREATE MODEL statement with option model_type='linear_reg' is used to create a linear regression model in BigQuery ML.

Answer

INSERT INTO model my_model VALUES ...

Answer

CREATE ML my_model AS (SELECT ...)

Answer

SELECT ML.TRAIN('linear_reg', ...)

Question 12

A retailer wants to generate personalized product descriptions using PaLM API. They have concerns about data privacy. What is the best practice to mitigate these concerns?

Accepted Answer

Enable data residency and use prompt engineering to avoid including personally identifiable information. Data residency ensures customer data is processed and stored within a specific geographic region, addressing regulatory compliance, while prompt engineering allows the retailer to avoid sending PII to the PaLM API entirely. This combination mitigates privacy risks without requiring custom model training or relying solely on encryption, which does not prevent the API from processing sensitive data.

Answer

Train a custom model from scratch on proprietary data stored on-premise

Answer

Use the PaLM API directly with anonymized customer data

Answer

Encrypt all data in transit and at rest using customer-managed encryption keys

Question 13

A healthcare startup is exploring GenAI for clinical note summarization. They have concerns about patient data privacy. Which Google Cloud approach best addresses privacy while still using powerful models?

Accepted Answer

Use Vertex AI with data residency controls and no external data sharing. Vertex AI with data residency controls and no external data sharing ensures that patient data remains within specified geographic boundaries and is not used for model training or improvement, directly addressing healthcare privacy regulations like HIPAA. This approach leverages Google Cloud's powerful models while maintaining strict data governance, unlike options that risk data exposure or lack enterprise-grade controls.

Answer

Deploy open-source models on-premises

Answer

Use a third-party API with anonymization of patient data

Answer

Use Vertex AI with model customization (fine-tuning)

Question 14

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.

Answer

Number of model versions

Answer

Number of model instances

Question 15

You are a Generative AI architect at a large financial services firm. The firm has deployed a custom large language model (LLM) fine-tuned on proprietary financial reports to assist analysts in generating quarterly earnings summaries. The model is hosted on Vertex AI using a dedicated endpoint with autoscaling enabled. Recently, the model's output has exhibited two issues: (1) occasional factual inaccuracies about specific financial figures, and (2) a tendency to produce overly verbose and repetitive text in the summaries, sometimes exceeding the desired length of 200 words. The team has already tried adjusting the temperature parameter from 0.7 to 0.2 and increased the top-k sampling from 40 to 50, but the problems persist. The model's training data includes over 10,000 financial reports, and the fine-tuning process used low-rank adaptation (LoRA) with rank 16. The production environment uses a batch size of 1 for inference. You need to recommend a course of action that most directly addresses both the factual accuracy and verbosity issues without requiring a full retraining of the model. Which approach should you take?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline that queries a vector database of verified financial data, and apply constrained decoding with a maximum token limit and a repetition penalty.. It directly addresses both issues without retraining. A RAG pipeline grounds the model's outputs in verified financial data, eliminating factual inaccuracies. Constrained decoding with a maximum token limit and repetition penalty directly curbs verbosity and repetition, which temperature and top-k adjustments failed to fix.

Answer

Increase the LoRA rank to 32 and fine-tune the model for additional epochs on a curated subset of reports that focus on concise and accurate summaries.

Answer

Switch to a larger pre-trained model (e.g., PaLM 2 or GPT-4) and use the same fine-tuning data with higher rank LoRA to improve capability, then rely on the larger model's inherent accuracy.

Answer

Experiment with higher temperature (e.g., 0.9) and lower top-k (e.g., 20) to encourage more diverse and concise outputs, and add a post-processing step to truncate summaries to 200 words.