The answer is support for predicate pushdown to skip irrelevant data, which is a core benefit of columnar storage formats like Parquet for analytical workloads. This is correct because Parquet stores data by column rather than by row, so when a query only needs a subset of columns—such as a SELECT SUM(sales) aggregation—the storage engine reads only the relevant column chunks from disk, dramatically reducing I/O compared to reading entire rows. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of how columnar formats optimize performance for large-scale analytics, often appearing in questions about data storage options in Azure Synapse or Azure Data Lake. A common trap is confusing row-based formats like CSV with columnar ones; remember that Parquet excels when queries filter or aggregate on specific columns, not when retrieving all fields for a single record. Memory tip: think “columnar cuts clutter”—it skips columns you don’t need, slashing I/O and speeding up queries.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE of the following are benefits of using a columnar storage format like Parquet for analytical workloads?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduced I/O when querying a subset of columns
Option B is correct because columnar storage formats like Parquet store data by column rather than by row. When a query only needs a subset of columns (e.g., SELECT SUM(sales) FROM table), the storage engine reads only the relevant column chunks from disk, dramatically reducing I/O compared to reading entire rows. This is a key performance advantage for analytical workloads that aggregate or filter on specific columns.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Enforced referential integrity constraints
Why it's wrong here
Referential integrity is a relational concept, not a feature of storage formats.
✓
Reduced I/O when querying a subset of columns
Why this is correct
Reading fewer columns reduces I/O and speeds up queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optimized for frequent row updates
Why it's wrong here
Columnar formats are not efficient for row-level updates; they are designed for read-heavy analytics.
✓
Better compression ratios due to similar data types in columns
Why this is correct
Columnar storage groups similar data, leading to high compression.
Related concept
Read the scenario before looking for a memorised answer.
✓
Support for predicate pushdown to skip irrelevant data
Why this is correct
Columnar formats allow query engines to read only necessary columns.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the benefits of columnar storage (optimized for read-heavy, aggregate queries) with row-oriented storage benefits (optimized for frequent updates and transactional integrity), leading them to incorrectly select Option C.
Detailed technical explanation
How to think about this question
Parquet uses a hybrid storage model: it organizes data into row groups, then within each row group stores columns contiguously. This allows for both column-level compression (e.g., dictionary encoding, run-length encoding) and predicate pushdown at the row group level via embedded min/max statistics. In real-world scenarios, a query filtering on a date column can skip entire row groups where the date range does not match, reducing I/O even further.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduced I/O when querying a subset of columns — Option B is correct because columnar storage formats like Parquet store data by column rather than by row. When a query only needs a subset of columns (e.g., SELECT SUM(sales) FROM table), the storage engine reads only the relevant column chunks from disk, dramatically reducing I/O compared to reading entire rows. This is a key performance advantage for analytical workloads that aggregate or filter on specific columns.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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