Databricks-Spark-Assoc Developing DataFrame/DataSet API Applications Practice Question
A developer has a DataFrame `raw` and needs to permanently persist it as Parquet partitioned by `region`, overwriting any existing data at that path, without registering it in the metastore. Which call achieves this?
⚠ Common exam trap
Watch out — candidates often confuse `saveAsTable`, which registers metastore metadata, with a direct path-based `write`, which does not.
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
✓
raw.write.mode("overwrite").partitionBy("region").parquet("/data/out")
The DataFrameWriter path with `mode("overwrite")`, `partitionBy("region")`, and `.parquet` writes typed Parquet files into per-region directories and replaces prior contents in one step. Because it targets a filesystem path rather than a table name, no metastore entry is created, satisfying the "without registering it in the metastore" constraint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
raw.rdd.saveAsTextFile("/data/out")
Why it's wrong here
`saveAsTextFile` operates on the underlying RDD and writes plain text with a string representation of each row, discarding schema and column types. It also cannot express partitioning by `region` or Parquet encoding, so downstream readers lose the typed columns and the directory layout entirely.
- ✗
raw.write.format("parquet").saveAsTable("out")
Why it's wrong here
`saveAsTable` registers a managed or external table in the metastore, which the developer explicitly wants to avoid, and it omits both partitioning and the overwrite mode. Without `partitionBy`, no region directory structure is produced, and without overwrite the write fails when the target already exists.
- ✗
raw.createOrReplaceTempView("out").write.parquet("/data/out")
Why it's wrong here
`createOrReplaceTempView` returns `None`, so chaining `.write` on it raises an `AttributeError` at runtime. The view is also session-scoped metadata only, not a persistence mechanism, and this snippet never applies partitioning or an overwrite mode, so it fails on every count in the scenario.
- ✓
raw.write.mode("overwrite").partitionBy("region").parquet("/data/out")
Why this is correct
This uses the DataFrameWriter with `mode("overwrite")` to replace existing data, `partitionBy("region")` to lay out Hive-style directories, and the `parquet` format sink to write files. It writes directly to the filesystem path without touching the metastore, which is precisely the requirement stated in the scenario.
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JA
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-Spark-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-Spark-Assoc exam.