Generative AI Leader Practice Test 2 — 25 Questions

Question 1

Refer to the exhibit. A user with this IAM role tries to deploy a model to a Vertex AI Endpoint but fails. What is the most likely reason?

Accepted Answer

The user needs the roles/aiplatform.deployer role. The user has an IAM role but lacks the specific permission `aiplatform.deployments.create` required to deploy a model to a Vertex AI Endpoint. The `roles/aiplatform.deployer` role includes this permission, while the user's existing role does not, causing the deployment to fail. Even if the user can use other Vertex AI services, deploying a model to an endpoint is a distinct action that requires this specific role.

Answer

The user is not authorized to use Vertex AI at all

Answer

The model artifact is not in the same region as the endpoint

Answer

The user needs the roles/aiplatform.admin role

Question 2

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

Answer

To improve training speed

Answer

To reduce model size for faster inference

Answer

To enable multi-modal inputs

Question 3

A company's generative AI model is producing biased outputs. What is the most effective mitigation strategy?

Accepted Answer

Fine-tune the model using a balanced, representative dataset and implement output filtering. Fine-tuning on a balanced, representative dataset directly addresses the root cause of biased outputs by correcting the model's learned associations, while output filtering provides a safety net to catch residual bias. This combination is more effective than superficial fixes because it modifies the model's internal weights rather than just masking outputs.

Answer

Use a larger model with more parameters to improve overall accuracy

Answer

Use prompt engineering to instruct the model to avoid biased language

Answer

Increase the diversity of input samples by random sampling

Question 4

A company wants to use Generative AI for customer support chatbots. They are concerned about cost and latency. Which deployment option best balances these concerns?

Accepted Answer

Use a fine-tuned version of a smaller model on Vertex AI with response caching. Option D is correct because using a fine-tuned smaller model on Vertex AI with response caching reduces both cost and latency. Smaller models require fewer computational resources, and caching avoids redundant inference calls, directly addressing the company's concerns without sacrificing accuracy for the specific task.

Answer

Deploy an open-source model on-premise to avoid cloud costs

Answer

Rely on a third-party chatbot API that abstracts the model

Answer

Use the largest available foundation model via API for highest accuracy

Question 5

Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)

Accepted Answer

Generative models can create new data samples, while discriminative models only assign labels to existing data.. Option A is correct because generative AI models learn the underlying distribution of the data, enabling them to generate new, realistic samples (e.g., images, text) from the learned distribution. In contrast, discriminative models learn decision boundaries to classify or label existing data without the ability to create new data instances. This fundamental difference in capability—creation versus discrimination—is a core distinction between the two paradigms.

Answer

Generative models require less training data than discriminative models.

Answer

Generative models cannot be used for supervised learning tasks like classification.

Answer

Discriminative models always outperform generative models on tasks like image classification.

Question 6

A team uses Vertex AI to host a large language model. They want to reduce latency for real-time applications. What is the best strategy?

Accepted Answer

Use model quantization. Option C is correct because model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, directly lowering inference latency for real-time applications on Vertex AI. This is a standard optimization technique for deploying large language models with minimal accuracy loss while meeting latency SLAs.

Answer

Increase number of replicas

Answer

Switch to a smaller model

Answer

Use batch prediction instead of online

Question 7

Which THREE of the following are common techniques to reduce harmful biases in generative AI models? (Choose three.)

Accepted Answer

Use reinforcement learning from human feedback (RLHF) with a reward model that penalizes biased or unfair outputs.. A is correct because RLHF uses a reward model trained on human preferences to score model outputs, and explicitly penalizing biased or unfair outputs during fine-tuning directly reduces harmful biases. This technique aligns the model's behavior with human values by optimizing against a learned reward signal that captures bias-related concerns.

Answer

Decrease the model's temperature parameter to make outputs more deterministic.

Answer

Conduct a legal review of all generated outputs before release.

Question 8

A company uses a Gemini 1.5 Pro model with a 1 million token context window. They want to process a large 500-page PDF for Q&A. What is the MAIN advantage of using the long context window over a RAG approach?

Accepted Answer

Simpler architecture with no need for chunking or retrieval systems. Option A is correct because the primary advantage of using a 1 million token context window is architectural simplicity. By ingesting the entire 500-page PDF as a single prompt, the company eliminates the need for document chunking, embedding generation, and a retrieval system (RAG). This reduces system complexity, maintenance overhead, and potential failure points, as the model can directly attend to all content in one pass.

Answer

Lower cost per API call

Answer

Faster inference speed

Answer

Higher accuracy for all queries

Question 9

A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?

Accepted Answer

Use more diverse data. Option C is correct because introducing more diverse data helps the model generalize better by exposing it to a wider variety of patterns, reducing the risk of memorizing noise from a limited dataset. Option E is correct because dropout randomly deactivates a fraction of neurons during fine-tuning, which prevents co-adaptation and acts as a regularization technique to combat overfitting in transformer-based models like Gemma.

Answer

Use a larger batch size

Answer

Increase the number of training epochs

Answer

Reduce the learning rate

Question 10

Which THREE of the following are potential risks when deploying generative AI?

Accepted Answer

Hallucinations. Option A is correct because generative AI models, particularly large language models (LLMs), can produce plausible-sounding but factually incorrect or nonsensical outputs, known as hallucinations. This occurs due to the model's probabilistic nature and lack of true understanding, where it generates text based on learned patterns rather than verified facts.

Answer

Memorization of sensitive training data

Answer

Increased model accuracy

Question 11

A company is using a generative AI model to generate product descriptions. They notice the outputs often include factual inaccuracies about product specifications. Which technique would best address this issue without modifying the model's architecture?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves product specs from a database. Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's output in factual, up-to-date product specifications retrieved from an external database. This directly addresses factual inaccuracies without modifying the model's architecture, as the model generates text based on retrieved context rather than relying solely on its parametric knowledge.

Answer

Decrease the temperature parameter to 0.1

Answer

Increase the max output tokens to 1024

Answer

Use few-shot prompting with 5 examples of correct descriptions

Question 12

What is the primary purpose of a system instruction in the Gemini API?

Accepted Answer

Define the overall behavior and constraints for the model. The system instruction in the Gemini API is the primary mechanism to define the overall behavior, persona, constraints, and guardrails for the model across all interactions. Unlike per-query parameters, it sets a persistent context that shapes how the model interprets every user prompt, ensuring consistent adherence to rules such as tone, format, or safety policies.

Answer

Set the model's temperature and top_p

Answer

Provide few-shot examples for each query

Answer

Set the maximum output length

Question 13

Which TWO techniques are effective for reducing bias in generative AI model outputs?

Accepted Answer

Training on diverse and representative datasets. Option B is correct because training on diverse and representative datasets directly reduces sampling bias and coverage gaps in the training distribution, which are primary sources of stereotypical or skewed outputs. By ensuring the model sees balanced examples across demographics, contexts, and edge cases, it learns more equitable representations and reduces the likelihood of generating biased content.

Answer

Increasing model size to learn more patterns

Answer

Relying solely on post-hoc filters

Answer

Limiting the model to only factual prompts

Question 14

What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

Accepted Answer

They compress text into dense vectors for efficient retrieval.. Option A is correct because embedding models convert text into dense vector representations that capture semantic meaning, enabling efficient similarity search in vector databases. This compression reduces the dimensionality of the data, allowing the RAG system to quickly retrieve the most relevant documents from a large corpus based on vector distance metrics like cosine similarity.

Answer

They allow the model to generate new training data automatically.

Answer

They provide deterministic outputs for the same query.

Question 15

A company is deploying a chatbot that uses a foundation model. They want to minimize latency for user queries. Which action is most effective?

Accepted Answer

Use a smaller distilled model. Distilled models are smaller, faster versions of larger foundation models, trained to mimic their behavior while requiring fewer computational resources. This directly reduces inference latency because fewer parameters mean faster forward passes through the network, which is critical for real-time chatbot responses.

Answer

Use a larger model with more parameters

Answer

Disable safety filters

Answer

Increase the number of tokens

Question 16

An organization wants to ensure their generative AI application does not produce toxic or harmful content. Which Vertex AI feature should they implement?

Accepted Answer

Safety filters and content moderation. Safety filters and content moderation in Vertex AI allow organizations to define and enforce policies that block or flag toxic, harmful, or inappropriate content generated by the model. This feature uses pre-built and customizable classifiers to evaluate prompts and responses against safety attributes (e.g., hate speech, harassment, sexually explicit content) before returning them to the user, directly addressing the requirement to prevent harmful outputs.

Answer

Explainable AI

Answer

AutoML

Answer

Model Monitoring

Question 17

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.. Option A is correct because 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 18

A team uses PaLM 2 API to generate product descriptions, but the output sometimes contains factual inaccuracies. What is the best approach to improve accuracy?

Accepted Answer

Use grounding with Google Search. Grounding with Google Search is the correct approach because it allows the PaLM 2 API to retrieve real-time, verifiable information from the web, directly reducing factual inaccuracies in generated product descriptions. Unlike parameter adjustments, grounding provides an external knowledge source that the model can cite, ensuring outputs are based on current and accurate data rather than relying solely on its training data.

Answer

Increase the temperature parameter

Answer

Reduce the top_k value

Answer

Set the max_output_tokens higher

Question 19

A research team is training a large language model from scratch using TPUs on Google Cloud. Which storage solution provides the highest throughput for training data?

Accepted Answer

Cloud Storage. Cloud Storage provides the highest throughput for training data because it is designed for high-bandwidth, parallel access from TPU pods via the Google Cloud Storage FUSE or gRPC-based data loading. TPUs benefit from Cloud Storage's ability to serve data at hundreds of GB/s when using the `tf.data` service with `tf.io.gfile` or the `gcloud storage` API, avoiding the I/O bottlenecks of block storage. Persistent Disk and Filestore have lower aggregate throughput limits and are not optimized for the distributed, streaming read patterns typical of large-scale training.

Answer

Persistent Disk

Answer

Cloud Filestore

Answer

Cloud Spanner

Question 20

A company has a large dataset of proprietary documents and wants to build a Q&A system using a foundation model without exposing the documents to the model. Which approach is most appropriate?

Accepted Answer

Use RAG with Vertex AI Vector Search and embeddings. Option A is correct because Retrieval-Augmented Generation (RAG) with Vertex AI Vector Search allows the system to retrieve relevant document chunks via embeddings without exposing the full documents to the foundation model. The model only sees the retrieved context in the prompt, ensuring proprietary data remains isolated and not used for training or memorization.

Answer

Use a zero-shot model with context in prompt

Answer

Fine-tune the model on the documents

Answer

Use prompt engineering to instruct the model

Generative AI Leader Practice Test 2 — 25 Questions

Exhibit

Generative AI Leader Practice Test 2 — 25 Questions

Exhibit