Databricks-GenAI-Assoc Data Preparation Practice Question
A data engineer is preparing a large corpus of customer support transcripts stored as Parquet in a Unity Catalog volume. Before generating embeddings, each transcript must be tokenized and truncated to a maximum token length. The engineer wants to use a Databricks-native approach that runs distributed across the cluster and avoids pulling the full corpus into a single node. Which approach best satisfies these requirements?
⚠ Common exam trap
The trap here is equating native Python tokenization with distributed execution, when only executor-side constructs like pandas UDFs keep the corpus distributed.
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
✓
Use spark.read.parquet to load the corpus and apply a pandas_udf that tokenizes and truncates each transcript.
Distributed tokenization of a large corpus requires the work to run inside Spark executors, which a pandas UDF provides by operating vectorized on each partition. Reading with pandas on the driver or looping over files locally centralizes the workload and breaks at scale. A pandas UDF keeps the corpus distributed while applying the tokenizer and truncation per row.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use spark.read.parquet to load the corpus and apply a pandas_udf that tokenizes and truncates each transcript.
Why this is correct
A pandas UDF runs vectorized tokenization inside Spark executors, so the corpus stays distributed and each partition processes its own rows. This avoids collecting the data to the driver and scales with cluster size. Tokenizing and truncating in the same distributed step is exactly the preparation needed before embedding generation.
- ✗
Use dbutils.fs.cp to copy the Parquet files to a local path, then run a Python tokenizer loop over each file.
Why it's wrong here
dbutils.fs.cp moves files between storage locations but does not distribute computation. A sequential Python loop over files executes on the driver, so tokenization remains single-threaded and memory-bound. This approach also adds unnecessary data movement without providing any distributed processing benefit.
- ✗
Create a Delta table from the Parquet files and query it with a SQL UDF that calls a tokenizer from a Python library.
Why it's wrong here
A SQL UDF cannot directly import and call arbitrary Python tokenizer libraries; Python logic in SQL requires a Python UDF wrapper. Even then, a scalar Python UDF processes row by row without vectorization, which is slower than a pandas UDF for tokenization workloads. The core distributed-processing goal is better met by a vectorized pandas UDF.
- ✗
Read the Parquet files with pandas.read_parquet on the driver, tokenize, then write the truncated text back to the volume.
Why it's wrong here
Loading the corpus with pandas on the driver concentrates all data and tokenization work on a single node. For a large transcript corpus this will exhaust driver memory and eliminate parallelism. It directly conflicts with the requirement to avoid pulling the full corpus onto one machine.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.