Fundamentals of Generative AI →easyMultiple ChoiceObjective-mapped

Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

apiVersion: aiplatform/v1
kind: Model
metadata:
  name: my-model
spec:
  baseModel: "publishers/google/models/gemini-1.5-pro"
  tuningPipeline:
    ...

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

⚠ Common exam trap

Google Cloud often tests the distinction between 'tuningPipeline' (fine-tuning an existing model) and 'trainingPipeline' (training from scratch), and the trap here is that candidates confuse fine-tuning with full training or assume the field is for inference tasks like prediction.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

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 analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
It specifies a pipeline to fine-tune the base model
Why this is correct
The tuningPipeline references a pipeline that performs supervised fine-tuning of the base model.
✗
It configures the model for online prediction
Why it's wrong here
Online prediction settings are defined in the endpoint deployment, not in the model definition.
✗
It defines the hyperparameters for training from scratch
Why it's wrong here
The baseModel indicates a pre-existing model, so tuningPipeline is for fine-tuning, not training from scratch.
✗
It sets the model for batch prediction
Why it's wrong here
Batch prediction configuration is separate; tuningPipeline is for training.

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Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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