The correct strategy is to use a directory structure that enables partition elimination. This works because Azure Data Lake Storage Gen2’s hierarchical namespace allows query engines like Azure Synapse Serverless SQL or Spark to prune entire directories at the storage layer. When data is organized under a path such as `/sensorID=123/date=2025-03-20/`, a query filtering for a specific sensor and the last 7 days will skip all directories that don’t match, dramatically reducing the amount of data scanned. On the DP-900 exam, this tests your understanding of how storage-level optimization differs from compute-level filtering—a common trap is assuming that indexing or caching alone minimizes scan volume. Remember the mnemonic: “Partition paths prune petabytes”—if your directory hierarchy mirrors your query filters, you eliminate data before the engine even reads it.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A company uses Azure Data Lake Storage Gen2 to store IoT sensor data. The data is partitioned by date and sensor ID. A data scientist needs to efficiently query only the last 7 days of data for a specific sensor. Which strategy minimizes the amount of data scanned?
Use a directory structure that enables partition elimination
By organizing data in a hierarchical directory structure (e.g., /sensorID=xxx/date=yyyy-mm-dd/), query engines can prune partitions and scan only relevant files.
B
Create a view that filters on date and sensor ID
Why wrong: A view does not improve performance; it only simplifies queries.
C
Read all Parquet files and filter using a WHERE clause
Why wrong: This approach scans all files, which is inefficient for large datasets.
D
Create an index on the date and sensor ID columns
Why wrong: Indexing is typically used in databases, not in data lake storage; Parquet files already have column statistics, but partition elimination is more effective.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a directory structure that enables partition elimination
Option A is correct because Azure Data Lake Storage Gen2 supports hierarchical directory structures that enable partition elimination at the storage layer. By organizing data under a path like `/sensorID=123/date=2025-03-20/`, a query engine (e.g., Azure Synapse Serverless SQL or Spark) can skip entire directories that do not match the filter, drastically reducing the amount of data scanned.
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.
✓
Use a directory structure that enables partition elimination
Why this is correct
By organizing data in a hierarchical directory structure (e.g., /sensorID=xxx/date=yyyy-mm-dd/), query engines can prune partitions and scan only relevant files.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a view that filters on date and sensor ID
Why it's wrong here
A view does not improve performance; it only simplifies queries.
✗
Read all Parquet files and filter using a WHERE clause
Why it's wrong here
This approach scans all files, which is inefficient for large datasets.
✗
Create an index on the date and sensor ID columns
Why it's wrong here
Indexing is typically used in databases, not in data lake storage; Parquet files already have column statistics, but partition elimination is more effective.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse database indexing (Option D) with data lake partitioning, or assume that a WHERE clause alone (Option C) is sufficient to minimize data scanned, not realizing that partition elimination requires a physical directory structure.
Detailed technical explanation
How to think about this question
Partition elimination in ADLS Gen2 works by leveraging the hierarchical namespace to map directory paths to partition columns. When a query filters on `sensorID` and `date`, the query engine (e.g., Synapse SQL or Spark) uses the directory structure to list only the relevant subdirectories, avoiding a full directory listing. This is especially effective with Hive-style partitioning (e.g., `year=2025/month=03/day=20/`), which allows granular skipping of entire date ranges without scanning file metadata.
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 an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a directory structure that enables partition elimination — Option A is correct because Azure Data Lake Storage Gen2 supports hierarchical directory structures that enable partition elimination at the storage layer. By organizing data under a path like `/sensorID=123/date=2025-03-20/`, a query engine (e.g., Azure Synapse Serverless SQL or Spark) can skip entire directories that do not match the filter, drastically reducing the amount of data scanned.
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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