Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
An analyst runs a notebook against an all-purpose cluster and notices that the first cell, which reads a large Delta table, takes several minutes while subsequent similar queries finish quickly. The analyst wants to understand why this happens and ensure the same quick response on later runs. Which explanation best describes the underlying behavior?
⚠ Common exam trap
The trap here is attributing the speedup to table optimization or an engine change, when the real cause is cluster startup latency combined with automatic disk caching of Delta data during the first read.
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
✓
The cluster had to start and initialize, and Delta caching kept data in memory for later queries
The slow first cell reflects two overlapping costs: the all-purpose cluster must start and initialize before any query runs, and the initial scan reads Delta files from remote storage. Once that scan completes, Databricks caches the data on local SSD, so later queries that touch the same files avoid remote reads. Warm compute plus caching explains the dramatic speedup on subsequent runs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The first query compiled a query plan that Databricks permanently stored in the table metadata
Why it's wrong here
Query plans are compiled and may be cached within a session or cluster, but they are not written into Delta table metadata. Table metadata stores schema, partitioning, and transaction log information, not execution plans. While plan reuse can reduce overhead, it does not explain a multi-minute first read, which points instead to cluster startup and data caching.
- ✗
The first query used a different execution engine, and later queries switched to Photon
Why it's wrong here
Photon is enabled or disabled by cluster configuration, not toggled between consecutive queries in the same session. If Photon were enabled, it would apply to the first query as well. The observed pattern of a slow first query followed by fast repeats is characteristic of cluster startup and data caching, not of an engine switch mid-session.
- ✗
The Delta table was automatically optimized after the first read, rewriting all files
Why it's wrong here
Reading a Delta table does not trigger automatic file rewriting. Optimize operations such as OPTIMIZE must be run explicitly, and even then they compact small files rather than rewrite on every read. The later queries were fast because of caching and warm compute, not because the table was silently reorganized during the first scan, so this explanation is incorrect.
- ✓
The cluster had to start and initialize, and Delta caching kept data in memory for later queries
Why this is correct
When an all-purpose cluster starts, it must provision resources and initialize the Spark and Databricks runtime, which delays the first query. As the first read scans the Delta table, Databricks caches the underlying files on the cluster's local SSD, so subsequent queries against the same data skip remote storage reads and complete much faster. This explains both the initial delay and the later speedup.
About these practice questions
This Databricks-DA-Assoc question is part of Courseiva's 291-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-DA-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-DA-Assoc exam.