Databricks DA-ASSOC Data Cleaning and Quality Guide
April 13, 2026
Study Databricks DA-ASSOC Data Cleaning and Quality: key concepts, common traps, and exam decision cues.
On this page
The exam treats governed data management as analyst work, not just platform admin work. You need to know how to start with the right dataset, clean it predictably, and avoid turning “data quality” into an excuse for hiding broken query logic.
Start with trusted data when possible
Better starting point
Why it usually wins
certified dataset
lowers trust risk and usually carries clearer business meaning
uncataloged raw table
can be useful, but adds validation burden
random copied export
often breaks lineage and trust unless the scenario explicitly requires it
Common cleaning patterns
Problem
Common SQL move
What to watch
missing values
COALESCE, null checks, filtered exclusion, or conditional substitution
do not change business meaning silently
invalid rows
filtered removal or standardized correction logic
make sure you are not removing valid edge cases
inconsistent formatting
normalized string or date handling
keep logic reproducible
duplicate-looking rows
verify row grain and join shape before deduplicating
DISTINCT is not a data-quality plan
What the exam is really testing
If the stem says…
Read it as…
“clean the data”
apply clear SQL handling for missing or invalid values
“trusted dataset”
prefer certified or governed assets when available
“wrong numbers after cleaning”
check whether you changed business logic or grain, not just null handling
Common traps
Trap
Better rule
deduplicating because numbers look wrong
validate grain and join logic first
replacing nulls without asking what they mean
null handling should fit the business rule
ignoring certified data because raw feels more flexible
exam stems often reward governed trust over ad hoc freedom
Decision order that usually wins
Data-prep questions usually reward starting from trusted governed assets before rewriting logic from scratch. If a certified dataset already exists, check whether its grain fits the need. If totals or row counts look wrong, inspect row grain and join shape before reaching for DISTINCT. DA-ASSOC usually prefers correctness and trust signals before clever cleanup tricks.