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← Fundamentals of Generative AI practice sets

Generative AI Leader Fundamentals of Generative AI • Complete Question Bank

Generative AI Leader Fundamentals of Generative AI — All Questions With Answers

Complete Generative AI Leader Fundamentals of Generative AI question bank — all 0 questions with answers and detailed explanations.

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Certifications/Generative AI Leader/Practice Test/Fundamentals of Generative AI/All Questions
Question 1easymultiple choice
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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?

Question 2mediummultiple choice
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A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

Question 3hardmultiple choice
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A financial services company wants to use generative AI to generate personalized investment advice. They must ensure responses comply with regulatory requirements (e.g., no guarantees of returns). Which Vertex AI safety feature should they primarily use?

Question 4mediummultiple choice
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A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

Question 5hardmultiple choice
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A team is fine-tuning a large language model on custom data using Vertex AI. They find that the training loss decreases but validation loss increases. What is the best course of action?

Question 6easymultiple choice
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A developer wants to generate product descriptions from a list of features using Vertex AI. Which model type is best suited for this task?

Question 7mediummultiple choice
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A company is using Vertex AI to generate email responses. They want to ensure sensitive customer data (PII) is not included in the output. What is the most effective approach?

Question 8easymulti select
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Which TWO statements are true about generative AI models?

Question 9mediummulti select
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A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

Question 10hardmulti select
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A team is evaluating generative AI models for a content moderation system. Which THREE metrics are most important to assess?

Question 11easymultiple choice
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A company wants to build a chatbot using Vertex AI that can answer customer questions based on their internal knowledge base. Which Google Cloud service should they use to store and retrieve the knowledge base efficiently?

Question 12mediummultiple choice
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A data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?

Question 13hardmultiple choice
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A company is deploying a generative AI application that generates medical reports. They need to ensure the output is factual and minimizes hallucinations. Which approach is most effective?

Question 14easymultiple choice
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A developer is using Vertex AI PaLM API to generate code snippets. The responses sometimes contain security vulnerabilities. What is the best practice to mitigate this?

Question 15mediummultiple choice
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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?

Question 16hardmultiple choice
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A company is using Vertex AI to generate personalized marketing emails. The model sometimes produces biased content. What is the most effective way to detect and mitigate bias?

Question 17mediummulti select
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Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Question 18hardmulti select
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Which THREE factors should be considered when choosing between fine-tuning and prompt engineering for a generative AI task? (Choose three.)

Question 19hardmultiple choice
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A data scientist sees the above error when trying to deploy a model to an endpoint. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
error: Vertex AI Model Registry: Model 'projects/my-project/locations/us-central1/models/123' has status 'DEPLOYING'. Cannot deploy a model that is not in 'READY' state.
```
Question 20mediummultiple choice
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A developer receives the above JSON response from a Vertex AI PaLM API call for a medical advice application. What should the developer be most concerned about?

Exhibit

Refer to the exhibit.

```
{
  "predictions": [
    {
      "content": "The patient's diagnosis is likely influenza, but further tests are needed.",
      "safetyAttributes": {
        "scores": [0.01],
        "blocked": false,
        "categories": ["health"]
      }
    }
  ],
  "deployedModelId": "123",
  "model": "projects/my-project/locations/us-central1/models/456"
}
```
Question 21mediummultiple choice
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You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

Question 22hardmultiple choice
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You are a data scientist at a financial institution. You are using Vertex AI to fine-tune a large language model (LLM) for generating financial reports. You have prepared a dataset of 10,000 examples. During fine-tuning, you notice that the training loss is decreasing steadily, but the validation loss is increasing after 5 epochs. The model's generated reports on the validation set contain many factual errors and nonsensical statements. You suspect overfitting. You have limited compute budget and need to improve generalization. What should you do?

Question 23hardmultiple choice
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A retail company is building a generative AI chatbot to assist customers with product recommendations and order tracking. The chatbot uses Vertex AI with Gemini 1.5 Pro, and the development team has implemented a Retrieval-Augmented Generation (RAG) pipeline using Vertex AI Search for grounding. The pipeline uses a vector store containing product descriptions and order history. During testing, the team observes that the chatbot sometimes provides incorrect order statuses—for example, claiming an order is 'shipped' when it is actually 'pending'. The team suspects the issue is related to how context is retrieved and used. The RAG pipeline currently retrieves the top 5 chunks based on cosine similarity from the vector store, and passes them as context to the model. The team is considering several changes to improve factual accuracy. Which single action would most effectively reduce hallucinations in this scenario?

Question 24easymultiple choice
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A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

Question 25mediummultiple choice
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A company fine-tunes a text model on internal HR policies. After deployment, the model sometimes outputs sensitive employee information. What is the most likely cause?

Question 26hardmultiple choice
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A developer uses the Vertex AI Python SDK to call a Gemini model for structured JSON output. However, the model often returns malformed JSON. Which parameter should the developer set in the generation configuration to enforce valid JSON output?

Question 27easymultiple choice
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A graphic design company wants to generate high-quality synthetic images for product mockups. Which Google Cloud generative AI service is most suitable?

Question 28mediummultiple choice
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A data scientist fine-tunes a foundation model on customer support transcripts. After evaluation, the model's responses are too formal. Which adjustment during fine-tuning is most likely to make responses more conversational?

Question 29hardmultiple choice
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An organization uses a fine-tuned model for medical diagnosis and must comply with HIPAA. Which measure is essential when deploying the model on Vertex AI?

Question 30easymultiple choice
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A prompt engineer wants to improve the model's adherence to a specific output format (e.g., always start with a greeting). Which technique should they try first?

Question 31mediummultiple choice
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During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

Question 32hardmultiple choice
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A team is training a custom foundation model using JAX on TPUs on Google Cloud. They encounter frequent Out of Memory (OOM) errors. Which action is most effective in resolving the OOM error?

Question 33mediummulti select
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A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?

Question 34mediummulti select
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What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

Question 35hardmulti select
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A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Question 36mediummultiple choice
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Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?

Network Topology
gcloud ai models listregion=us-central1id: 123456789displayName: test-modellabels: {}deployedModels: []etag: abc123
Question 37hardmultiple choice
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Refer to the exhibit. This IAM policy is applied to a Vertex AI project. A user 'test@example.com' reports they cannot create a ModelEvaluationPipelineJob. Which action should the administrator take?

Exhibit

{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": ["user:test@example.com"]
    },
    {
      "role": "roles/aiplatform.admin",
      "members": ["serviceAccount:sa@project.iam.gserviceaccount.com"]
    }
  ]
}
Question 38easymultiple choice
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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?

Exhibit

Error: 403 Permission 'aiplatform.endpoints.predict' denied on resource 'projects/my-project/locations/us-central1/endpoints/my-endpoint'.
Question 39easymultiple choice
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A startup wants to use a pre-trained model to generate product descriptions without training. Which Google Cloud service should they use?

Question 40mediummultiple choice
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A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

Question 41hardmultiple choice
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A gen AI application produces hallucinations (factually incorrect outputs). Which mitigation strategy is LEAST effective?

Question 42easymultiple choice
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What is the purpose of grounding in Vertex AI?

Question 43mediummultiple choice
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A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

Question 44hardmultiple choice
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A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Question 45easymultiple choice
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Which Google Cloud product provides access to pre-trained foundation models like Gemini?

Question 46mediummultiple choice
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A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?

Question 47hardmultiple choice
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Which of the following is a best practice when using Vertex AI for prompt engineering?

Question 48mediummulti select
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Which TWO are benefits of using retrieval-augmented generation (RAG) over fine-tuning?

Question 49hardmulti select
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Which THREE are valid methods to reduce bias in generative AI outputs?

Question 50easymulti select
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Which TWO are components of the Vertex AI Generative AI Studio?

Question 51mediummultiple choice
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Refer to the exhibit. A developer runs this command. What is the primary purpose?

Network Topology
gcloud ai models uploadcontainer-image-uri=gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-12:latestdisplay-name=my_modelartifact-uri=gs://my-bucket/model
Question 52hardmultiple choice
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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?

Exhibit

{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": [
        "user:user@example.com"
      ]
    }
  ]
}
Question 53easymultiple choice
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Refer to the exhibit. What is the most likely cause of this error?

Exhibit

ERROR: (gcloud.aiplatform.models.upload) PERMISSION_DENIED: Permission 'aiplatform.models.upload' denied on resource project my-project
Question 54easymultiple choice
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A company wants to use generative AI to summarize customer support tickets. Which Google Cloud tool is best suited for this task?

Question 55mediummultiple choice
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A developer is using Vertex AI Gemini API for a chatbot. The chatbot sometimes outputs harmful content. What is the best first step to mitigate this?

Question 56hardmultiple choice
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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?

Question 57mediummultiple choice
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A data scientist notices that a Gemini model generates inconsistent responses to similar prompts. What is the likely cause?

Question 58easymultiple choice
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A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

Question 59hardmultiple choice
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A team is building a medical diagnosis assistant using a foundation model. To comply with regulations, they need to ensure the model does not make up facts. What is the best approach?

Question 60mediummultiple choice
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After fine-tuning a foundation model on company emails, the model outputs confidential information. What is the most likely cause?

Question 61easymultiple choice
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A developer wants to quickly experiment with different foundation models available in Google Cloud. Which tool should they use?

Question 62hardmultiple choice
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A company is deploying a chatbot that uses a foundation model. They want to minimize latency for user queries. Which action is most effective?

Question 63mediummulti select
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A team is evaluating generative AI models on Vertex AI. They need to compare models based on specific criteria. Which TWO criteria are most important for selecting a model for a text summarization task?

Question 64hardmulti select
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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?

Question 65easymulti select
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Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?

Question 66mediummultiple choice
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Refer to the exhibit. A developer creates a model resource with this YAML config but gets an error that the model is not deployable. What is missing?

Exhibit

model:
  name: "projects/my-project/locations/us-central1/models/1234"
  explanation_spec:
    metadata:
      inputs:
        my_input:
          input_tensor_name: "input"
          modality: "text"
Question 67hardmultiple choice
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Refer to the exhibit. A developer sees this error when trying to deploy a model from Vertex AI Model Registry. What is the most likely cause?

Exhibit

Error: Root model provided is not a valid model reference. Please provide a model reference in the format of "projects/{project}/locations/{location}/models/{model}" or "projects/{project}/locations/{location}/models/{model}:{version}".
Question 68easymultiple choice
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Refer to the exhibit. A developer runs this command but forgets to specify the model name. What will happen?

Network Topology
gcloud ai models uploadcontainer-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-12:latest
Question 69easymultiple choice
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A company is using Vertex AI to deploy a text generation model for a chatbot. They want to reduce the response latency. Which configuration change is most effective?

Question 70easymultiple choice
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A data scientist needs to fine-tune a foundation model for a sentiment analysis task without managing infrastructure. Which Google Cloud service should they use?

Question 71easymultiple choice
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An organization wants to ensure their generative AI application does not produce toxic or harmful content. Which Vertex AI feature should they implement?

Question 72mediummultiple choice
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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?

Question 73mediummultiple choice
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A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?

Question 74mediummultiple choice
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During model evaluation, a team observes good performance on training data but poor on validation data. Which regularization technique is most appropriate to address this?

Question 75hardmultiple choice
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A financial institution deploys a chatbot using Gemini Pro in Vertex AI. Compliance requires logging all user inputs and model outputs for audit. Which approach meets this requirement?

Question 76hardmultiple choice
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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?

Question 77hardmultiple choice
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An MLOps engineer wants to implement continuous evaluation of a generative model in production. Which Vertex AI component should they use?

Question 78mediummulti select
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A company is designing a prompt engineering strategy for a customer service chatbot using Gemini. Which two practices are recommended for improving response quality? (Choose TWO)

Question 79easymulti select
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A developer is using Vertex AI Studio to test a text generation model. Which two actions can be performed in Vertex AI Studio? (Choose TWO)

Question 80hardmulti select
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A company is migrating an on-premises NLP pipeline to Vertex AI. Which three capabilities of Vertex AI align with common MLOps best practices for generative AI? (Choose THREE)

Question 81mediummultiple choice
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Refer to the exhibit. A developer executed the command to list endpoints. They notice that two models are deployed to the same endpoint. What is the most likely reason for this configuration?

Network Topology
gcloud ai endpoints listregion=us-central1Output:ENDPOINT_ID: 123456DISPLAY_NAME: my-endpointMODEL: projects/123/locations/us-central1/models/789DEPLOYED_MODELS:MACHINE_TYPE: n1-standard-2ACCELERATOR_TYPE: NVIDIA_TESLA_T4
Question 82hardmultiple choice
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Refer to the exhibit. An administrator creates this IAM policy for a Vertex AI project. What is the effect of this policy?

Exhibit

{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": ["user:alice@example.com"]
    },
    {
      "role": "roles/aiplatform.customCodeModelAdmin",
      "members": ["user:bob@example.com"]
    }
  ]
}
Question 83easymultiple choice
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Refer to the exhibit. A machine learning engineer is configuring a model using this YAML. What is the purpose of the 'tuningPipeline' field?

Exhibit

apiVersion: aiplatform/v1
kind: Model
metadata:
  name: my-model
spec:
  baseModel: "publishers/google/models/gemini-1.5-pro"
  tuningPipeline:
    ...
Question 84mediummultiple choice
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A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Question 85hardmultiple choice
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A developer is using Vertex AI Generative AI Studio to fine-tune a PaLM 2 model for code generation. After training, they notice the model generates plausible but incorrect code. What is the most likely cause?

Question 86easymultiple choice
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A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?

Question 87easymultiple choice
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A company is deploying a generative AI model for medical advice. What is the most important consideration?

Question 88mediummultiple choice
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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?

Question 89hardmultiple choice
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A company has fine-tuned a foundation model on proprietary data. During evaluation, they find the model performs well on seen examples but poorly on unseen but similar tasks. What is the problem?

Question 90easymultiple choice
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Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

Question 91mediummultiple choice
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A company wants to generate images from text descriptions. Which model in Vertex AI Model Garden should they use?

Question 92hardmultiple choice
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During fine-tuning a model on Vertex AI, the job fails with error 'ResourceExhausted: Out of memory'. What is the most likely cause?

Question 93mediummulti select
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Which TWO of the following are best practices for prompt engineering?

Question 94hardmulti select
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Which THREE of the following are potential risks when deploying generative AI?

Question 95easymulti select
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Which TWO are benefits of using pre-trained foundation models instead of training from scratch?

Question 96mediummultiple choice
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Refer to the exhibit. A team has deployed a model to an endpoint with the configuration shown. They notice that during peak traffic, the endpoint frequently returns 429 (Too Many Requests) errors. Which action should they take to resolve this issue?

Exhibit

Refer to the exhibit.
```
$ gcloud ai endpoints list --region=us-central1
ENDPOINT_ID: 123456789
DISPLAY_NAME: my-endpoint
MODEL: projects/my-project/locations/us-central1/models/987654321
DEPLOYED_MODELS: projects/my-project/locations/us-central1/models/987654321@1
MACHINE_TYPE: n1-standard-2
MIN_REPLICA_COUNT: 1
MAX_REPLICA_COUNT: 5
```
Question 97easymultiple choice
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Refer to the exhibit. A company has this IAM policy on a Vertex AI project. Alice complains she cannot create a new model. What is the most likely reason?

Exhibit

Refer to the exhibit.
```
{
  "bindings": [
    {
      "role": "roles/aiplatform.user",
      "members": ["user:alice@example.com"]
    },
    {
      "role": "roles/aiplatform.admin",
      "members": ["user:bob@example.com"]
    }
  ]
}
```
Question 98hardmultiple choice
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Refer to the exhibit. A data scientist is fine-tuning a model. The training loss and accuracy are improving each epoch. However, after training, the model performs poorly on a held-out validation set. What is the most likely issue?

Exhibit

Refer to the exhibit.
```
INFO: Training started.
INFO: Epoch 1/10 - loss: 2.3456 - accuracy: 0.1234
INFO: Epoch 2/10 - loss: 1.9876 - accuracy: 0.2345
INFO: Epoch 3/10 - loss: 1.6543 - accuracy: 0.3456
INFO: Epoch 4/10 - loss: 1.4321 - accuracy: 0.4567
INFO: Epoch 5/10 - loss: 1.2345 - accuracy: 0.5678
INFO: Epoch 6/10 - loss: 1.0987 - accuracy: 0.6789
INFO: Epoch 7/10 - loss: 0.9876 - accuracy: 0.7890
INFO: Epoch 8/10 - loss: 0.8765 - accuracy: 0.8901
INFO: Epoch 9/10 - loss: 0.7654 - accuracy: 0.9012
INFO: Epoch 10/10 - loss: 0.6543 - accuracy: 0.9123
```
Question 99easymultiple choice
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A startup is building a customer service chatbot that generates responses in real-time. They want the model to have up-to-date information on the latest product catalog but cannot afford frequent fine-tuning. Which technique should they use to inject current data into the model without retraining?

Question 100easymultiple choice
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A medical imaging team wants to generate synthetic X-ray images to augment a training dataset for a rare disease. Which type of generative model is most suitable for generating high-fidelity, realistic medical images?

Question 101mediummultiple choice
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An enterprise deploys a large language model (LLM) for internal document summarization. Users complain that summaries sometimes include statements not present in the original document. Which mitigation strategy should the team prioritize to address this hallucination issue?

Question 102mediummultiple choice
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A company is developing a code generation assistant and wants to ensure the model respects access control policies, e.g., it should not generate code that uses internal APIs that the user is not authorized to access. Which technique is most effective for embedding such policy constraints into the model's behavior?

Question 103mediummultiple choice
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A team is tuning a large language model for a question-answering task. They notice the model gives high confidence scores to answers that are factually incorrect. Which evaluation metric should they primarily use to detect this overconfidence problem?

Question 104hardmultiple choice
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A generative AI model is trained on a dataset containing biased text. The team wants to debias the model without significantly sacrificing performance on the original task. Which approach is most appropriate?

Question 105hardmultiple choice
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A multimodal generative AI system processes both image and text inputs to produce captions. During inference, the image encoder sometimes produces noisy or missing features. Which architectural design decision best handles such input degradation without retraining?

Question 106hardmultiple choice
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An organization wants to use a generative model to automatically generate legal contracts. The model must produce clauses that are not only grammatically correct but also legally enforceable and consistent with current jurisdiction laws. Which combination of techniques best ensures legal compliance?

Question 107easymulti select
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Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)

Question 108mediummulti select
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Which THREE of the following are common techniques to reduce harmful biases in generative AI models? (Choose three.)

Question 109hardmulti select
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Which THREE of the following are key considerations when deploying a generative AI model in a production environment with strict latency requirements? (Choose three.)

Question 110easymultiple choice
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You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

Question 111mediummultiple choice
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You are the lead AI engineer at a financial services firm. You have fine-tuned a large language model on historical trade reports to generate daily market summaries. The model is deployed on Google Cloud's Vertex AI using a custom container. A few weeks after deployment, the operations team notices that inference latency has increased by 300%, causing timeouts. You investigate and find that the model's memory consumption has grown unexpectedly, and the GPUs are idling due to high data transfer wait times. The model architecture and code have not changed. Which action is most likely to resolve the latency issue?

Question 112hardmultiple choice
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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?

Question 113mediummultiple choice
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You are the AI lead at an e-commerce company that uses a generative model to write product descriptions from images and key attributes. The model is a multimodal transformer that encodes both image and text (attributes) and decodes a description. Recently, your team deployed a new version of the image encoder that uses a more powerful backbone (ViT-L instead of ViT-B). After deployment, the generated descriptions became longer but often include irrelevant visual details (e.g., background objects) and occasionally misrepresent the product's main features. The model was fine-tuned on the same dataset as before. The descriptions from the old model were concise and focused. What is the most likely cause of the degradation and the best fix?

Question 114mediummulti select
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A company is building a conversational AI using the Gemini API on Vertex AI. They want to reduce the chance of generating toxic content while still allowing creative and engaging responses for their gaming community. Which TWO safety settings should they adjust in the safety_settings parameter?

Question 115easymultiple choice
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A startup is developing a customer support chatbot using Vertex AI PaLM 2 API. They notice that the model sometimes generates plausible-sounding but factually incorrect information about company policies. The chatbot currently uses no external data. To reduce these hallucinations without retraining the model, the team needs a solution that can be implemented quickly and maintains low latency. They have access to the company's internal policy database stored in Cloud SQL. Which approach should they take?

Question 116easymultiple choice
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A marketing team wants to generate product descriptions using a text generation model on Vertex AI. They need consistent output style across all descriptions, including tone and length. They have a small set of 10 high-quality example descriptions that capture the desired style. The team has limited ML expertise and wants a quick solution that does not require model retraining. Which approach should they use?

Question 117mediummultiple choice
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A healthcare company is building a clinical decision support system using Gemini 1.5 Pro on Vertex AI. They need responses that are highly accurate and comply with medical regulations, including traceability to source documents. They have a large corpus of curated medical guidelines stored in PDFs in Cloud Storage. Their team has experience with both fine-tuning and prompt engineering. Which approach best ensures regulatory compliance and accuracy?

Question 118hardmultiple choice
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A company is deploying a Gemini 1.0 Ultra model for a code generation assistant. They have set up Vertex AI Model Evaluation with a custom evaluation dataset to measure pass@1 accuracy. The initial evaluation shows 65% pass@1. They want to improve to 80% without collecting more training data. They have already attempted basic prompt engineering (e.g., 'write correct code') with limited improvement. Which approach is most likely to achieve the desired improvement?

Question 119hardmultiple choice
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A large enterprise runs a production application that uses the Gemini API on Vertex AI for real-time content moderation. They are experiencing occasional 429 (Too Many Requests) errors during peak hours. Their current quota is 1000 requests per minute (RPM) and they are hitting around 950 RPM on average, with spikes up to 1050. They have already implemented exponential backoff and retry logic. They need to reduce the error rate without reducing the quality of moderation. Which additional measure should they take?

Question 120hardmultiple choice
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A financial services company is building a customer service agent using Vertex AI Agent Builder. They want the agent to only answer questions based on their approved policy documents, which are stored in Cloud Storage. They also need to ensure that the agent never reveals internal employee names or account numbers. They have set up grounding with the documents but find that the agent sometimes ignores the grounding and generates responses using the model's internal knowledge. What should they do to strictly constrain the agent to only use the provided documents?

Question 121mediummultiple choice
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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?

Question 122easymulti select
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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?

Question 123hardmultiple choice
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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?

Exhibit

Refer to the exhibit.

```
$ gcloud ai endpoints predict 789 --region=us-central1 --json-request='{"instances":[{"content":"What is the capital of France?"}]}'
{
  "error": "Model does not support the given instance format."
}
```
Question 124mediummultiple choice
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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?

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