The answer is partitioning the data by date and using a columnar format like Parquet. Partitioning by date directly aligns with time-range queries, allowing the query engine to skip entire partitions that fall outside the requested window, which dramatically reduces the data scanned and speeds up performance. Columnar formats store data by column rather than by row, so when you query only a few columns—common in IoT telemetry analysis—the engine reads only those specific columns, minimizing I/O and enabling better compression, which lowers storage costs. On the DP-203 exam, this scenario tests your understanding of Azure Synapse Analytics and Azure Data Lake Storage optimization patterns, often appearing as a design question for high-velocity append-only workloads. A common trap is choosing row-based formats like CSV or JSON, which scan entire rows even for narrow queries. Memory tip: think “Partition to prune, Parquet to prune columns”—both reduce what you read, saving time and money.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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.
You need to design a storage solution for IoT device telemetry data that will be queried by time range. The data is append-only and arrives at high velocity. Which TWO features should you use to optimize query performance and reduce costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Store data in columnar format (e.g., Parquet)
Columnar formats like Parquet store data by column rather than by row, which significantly reduces I/O when querying only a subset of columns (common in time-range queries). This compression and column pruning directly lowers storage costs and speeds up scan-heavy analytical queries on append-only IoT telemetry data.
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.
✓
Store data in columnar format (e.g., Parquet)
Why this is correct
Columnar format reduces I/O and improves compression.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create indexes on all columns
Why it's wrong here
Indexing all columns increases write cost and storage.
✗
Enable row-level security
Why it's wrong here
Row-level security is for access control, not performance.
✓
Partition the data by date
Why this is correct
Time-based partitioning allows query pruning.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable geo-redundant storage
Why it's wrong here
Geo-redundancy increases cost and does not improve query performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse indexing (B) with partitioning, but for append-only analytical workloads, indexes add write overhead and cost without benefit, while date partitioning directly enables partition elimination for time-range queries.
Detailed technical explanation
How to think about this question
Parquet uses dictionary encoding, run-length encoding, and predicate pushdown to skip irrelevant row groups during scans. Partitioning by date (e.g., yyyy-MM-dd) allows query engines like Azure Synapse or Spark to perform partition pruning, eliminating entire directories from the scan when filtering by time range. This combination is standard in data lake architectures for time-series IoT workloads.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Store data in columnar format (e.g., Parquet) — Columnar formats like Parquet store data by column rather than by row, which significantly reduces I/O when querying only a subset of columns (common in time-range queries). This compression and column pruning directly lowers storage costs and speeds up scan-heavy analytical queries on append-only IoT telemetry data.
What should I do if I get this DP-203 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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