Generative AI Leader Practice Test 42 — 15 Questions

Question 1

Refer to the exhibit. A machine learning engineer is configuring a model using this YAML. What is the purpose of the 'tuningPipeline' field?

Accepted Answer

It specifies a pipeline to fine-tune the base model. The 'tuningPipeline' field in this YAML configuration specifies a dedicated pipeline for fine-tuning the base model, which is a common practice in MLOps frameworks like Vertex AI Pipelines or Kubeflow. It allows the engineer to define a separate workflow for parameter-efficient fine-tuning (e.g., LoRA) or full fine-tuning, distinct from training from scratch or serving. This field is essential for orchestrating the fine-tuning process, including data preprocessing, training, and evaluation steps, without affecting the base model's original weights.

Answer

It configures the model for online prediction

Answer

It defines the hyperparameters for training from scratch

Answer

It sets the model for batch prediction

Question 2

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

Accepted Answer

Use Vertex AI grounding with a knowledge base of verified entities. Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.

Answer

Enable response caching

Answer

Increase the temperature parameter

Answer

Decrease top_p to 0.3

Question 3

Refer to the exhibit. A developer runs this command but forgets to specify the model name. What will happen?

Accepted Answer

The command will fail with an error. In the context of the `gcloud ai models upload` command (or similar model deployment commands in Vertex AI), the model name is a required positional argument. If omitted, the CLI will fail with an error because it cannot proceed without a unique identifier to register the model in the model registry. The command does not default to any name or prompt interactively; it strictly validates required parameters before execution.

Answer

The command will prompt for a name

Answer

The model will be uploaded with a default name

Answer

The command will succeed but the model will be unlisted

Question 4

A company wants to build a text-to-speech application for generating voiceovers in multiple languages. They need to use a pre-built Google API without training custom models. Which service should they use?

Accepted Answer

Cloud Text-to-Speech API. Cloud Text-to-Speech API is a pre-built service for converting text to natural-sounding speech in many languages. The other options are either for speech recognition or not pre-built APIs.

Answer

Vertex AI Text-to-Speech with a custom model

Answer

Gemini API with a custom prompt

Answer

Cloud Speech-to-Text API

Question 5

A healthcare company needs to process medical records (e.g., discharge summaries) to extract structured data. Which AI API is specifically designed for this purpose?

Accepted Answer

Document AI with Healthcare NLP. Healthcare AI is purpose-built for medical documents, providing features like entity extraction for clinical data.

Answer

Natural Language AI

Answer

Translation AI

Answer

Vision AI

Question 6

A developer wants to use Gemini for multimodal tasks involving images and text. Which two features are available via the Gemini API on Vertex AI? (Choose two.)

Accepted Answer

Function calling. Gemini on Vertex AI supports multimodal input (image+text) and function calling.

Answer

Fine-tuning with images

Answer

Image generation

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Code execution

Question 7

A company is evaluating SLA guarantees for a generative AI model deployed on Vertex AI. They require at least 99.9% uptime for production inference. Which SLA tier should they select?

Accepted Answer

Regional endpoint with at least one node. Regional endpoints with at least one node guarantee a minimum of 99.9% uptime for production inference because they maintain a dedicated, always-on compute resource. This SLA applies to regional endpoints with a minimum of one replica, ensuring the model is continuously available for serving requests without cold-start delays.

Answer

Serverless endpoint with min replicas=0

Answer

Batch prediction job

Answer

Global endpoint with automatic scaling

Question 8

An organization is running a large-scale training job for a custom NLP model with a batch size of 2048 and sequence length of 512. They need to minimize training time while keeping costs predictable. Which Google Cloud hardware should they choose?

Accepted Answer

Cloud TPU v5e pods. Cloud TPU v5e pods are purpose-built for large-scale training of transformer-based NLP models, offering high-throughput matrix multiplication and efficient scaling across multiple chips. With a batch size of 2048 and sequence length of 512, TPU v5e pods deliver superior training speed and predictable pricing via reserved capacity, minimizing time-to-train compared to GPU alternatives.

Answer

Compute Engine with NVIDIA A100 GPUs

Answer

Edge TPU devices

Answer

Compute Engine with NVIDIA T4 GPUs

Question 9

An enterprise wants to use Gemini for a customer-facing application. They require the following: data isolation in a VPC, audit logging, and SLA guarantees. Which THREE features of Vertex AI satisfy these requirements?

Accepted Answer

Vertex AI SLA (Service Level Agreement). Vertex AI offers a defined Service Level Agreement (SLA) that guarantees uptime and performance metrics for enterprise customers, which is a core requirement for customer-facing applications. The SLA provides contractual assurances, typically covering availability and response times, ensuring the enterprise can meet its own service commitments.

Answer

Gemini Nano on-device

Answer

Google AI Studio free tier

Question 10

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 11

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

Accepted Answer

The top_p or temperature parameters are set too high causing randomness. High temperature (e.g., >1.0) or high top_p (e.g., >0.9) increases the randomness of token sampling, causing the model to select less probable tokens. This directly leads to inconsistent responses for similar prompts, as the model's output distribution becomes more uniform and less deterministic.

Answer

Model is not fine-tuned enough

Answer

The prompt is too short

Answer

The temperature setting is too low

Question 12

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. The `gcloud ai endpoints update-traffic-split` command is the dedicated command for modifying traffic splits between deployed models on a Vertex AI endpoint. It uses the `--traffic-split` flag with key-value pairs (model_id=percentage) to assign traffic percentages, ensuring the total sums to 100. This command directly updates the routing configuration without redeploying models.

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 13

A company deployed a Gemini model on Vertex AI for real-time inference. After a week, they notice that some requests return 500 Internal Server Error, and the endpoint is occasionally unreachable. The endpoint is configured with minReplicaCount=1 and maxReplicaCount=2. What is the most likely cause?

Accepted Answer

The endpoint is under-provisioned: minReplicaCount=1 is too low for peak load, causing the single replica to become saturated.. With minReplicaCount=1 and maxReplicaCount=2, the endpoint starts with a single replica. Under peak load, that single replica can become saturated (CPU/memory exhaustion), causing 500 errors and unreachability. Autoscaling can add a second replica, but if the traffic spike is sudden or the scaling metric takes time to trigger, the single replica is overwhelmed before the second instance is provisioned.

Answer

Autoscaling is disabled, so the endpoint cannot handle traffic spikes.

Answer

The model was updated while the endpoint was serving requests.

Answer

The project has reached its Vertex AI endpoint quota.

Question 14

A company wants to extract structured data from scanned invoices. Which Google Cloud AI service is purpose-built for this use case?

Accepted Answer

Document AI. Document AI is designed for document understanding, including invoice parsing.

Answer

Vision AI

Answer

Translation API

Answer

Natural Language AI

Question 15

A developer is building a mobile app that needs to run an AI model on-device for low-latency inference even without internet. Which Gemini model variant is designed for on-device deployment?

Accepted Answer

Gemini Nano. Gemini Nano is optimized for on-device execution on mobile devices.

Answer

Gemini Pro

Answer

Gemini Ultra

Answer

Gemini Flash