Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A team wants to fine-tune a PaLM 2 model with their own data on Vertex AI. What is the recommended way to prepare the training data?
⚠ Common exam trap
Many candidates assume CSV is universally supported for ML training data, but Vertex AI's PaLM 2 fine-tuning requires JSON Lines for its explicit key-value structure, which avoids CSV's limitations with multiline text and special characters.
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
✓
JSON Lines file with 'input_text' and 'output_text' keys
The recommended format for fine-tuning PaLM 2 on Vertex AI is a JSON Lines file with 'input_text' and 'output_text' keys. This structure directly maps to the model's expected supervised fine-tuning (SFT) input, where each line represents a single training example with a prompt and its corresponding response. Vertex AI's fine-tuning pipeline is optimized to parse this schema, ensuring correct tokenization and alignment during training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
TFRecord files
Why it's wrong here
TFRecord is for TensorFlow, not Vertex AI PaLM fine-tuning.
- ✓
JSON Lines file with 'input_text' and 'output_text' keys
Why this is correct
JSONL with the correct keys is required.
- ✗
CSV file with prompt and completion columns
Why it's wrong here
Vertex AI fine-tuning requires JSONL format, not CSV.
- ✗
Pickle serialized objects
Why it's wrong here
Pickle is not supported for Vertex AI training data.
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