Question 406 of 982
Describe an analytics workload on AzurehardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to switch to Azure Synapse dedicated SQL pool with proper table partitioning. This is correct because dedicated SQL pools support partition elimination, also known as partition pruning, where the query engine reads only the partitions matching the WHERE clause filter—in this case, specific date ranges—dramatically reducing I/O and scan time. In contrast, Azure Synapse Serverless SQL pool does not perform partition elimination on the underlying Parquet files, so it scans all partitions even with a date filter, causing slow performance on billions of rows. On the DP-900 exam, this scenario tests your understanding of the key difference between serverless and dedicated pools: serverless is ideal for ad-hoc exploration but lacks partition pruning, while dedicated pools are built for high-performance, predictable analytics with partitioning. A common trap is assuming serverless pools automatically benefit from file-level partitioning. Remember the mnemonic: “Dedicated does the pruning; serverless scans the whole blooming garden.”

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 financial institution runs complex analytical queries on trading data stored in Parquet files in Azure Data Lake Storage Gen2. The data is partitioned by date and contains billions of rows. Analysts frequently query within a specific date range, and the queries must return results in under 5 seconds. The current solution uses Azure Synapse Serverless SQL pool, but queries are slow because the serverless pool scans all partitions even when the WHERE clause filters on the date column. Which optimization should be implemented to improve query performance?

Question 1hardmultiple choice
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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

Switch to Azure Synapse dedicated SQL pool with proper table partitioning

Azure Synapse Serverless SQL pool does not support partition elimination based on the partitioning of the underlying Parquet files in Azure Data Lake Storage Gen2. By switching to an Azure Synapse dedicated SQL pool with proper table partitioning on the date column, the query engine can perform partition pruning, scanning only the relevant partitions for the specified date range, which drastically reduces I/O and improves query performance to meet the sub-5-second requirement.

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.

  • Switch to Azure Synapse dedicated SQL pool with proper table partitioning

    Why this is correct

    Correct. Dedicated SQL pools support partition elimination, allowing queries to skip scanning partitions that don't match the filter, dramatically improving performance.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Create a clustered columnstore index on the external table

    Why it's wrong here

    Incorrect. Clustered columnstore indexes cannot be created on external tables in Synapse Serverless; they apply only to tables in a dedicated pool.

  • Convert the Parquet files to CSV format

    Why it's wrong here

    Incorrect. CSV files lack columnar compression and encoding, making queries slower than Parquet, especially with billions of rows.

  • Use Azure Databricks with Delta Lake for querying

    Why it's wrong here

    Incorrect. While Databricks can improve performance with Delta Lake, this changes the query tool and does not address the partition elimination issue in Synapse Serverless as directly.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may assume serverless SQL pool automatically performs partition elimination on folder-partitioned data, but it does not; it scans all files unless explicit filepath() filtering is used, making dedicated SQL pool with table partitioning the correct choice for guaranteed partition pruning.

Detailed technical explanation

How to think about this question

Partition elimination in dedicated SQL pool works by storing metadata about partition boundaries, allowing the query optimizer to skip entire partitions that do not match the WHERE clause filter. In contrast, serverless SQL pool treats the folder structure as a file list and does not natively prune partitions based on folder names unless you use file metadata functions like filepath() in the query, which is not automatic. This distinction is critical for large-scale analytical workloads where partition pruning can reduce data scanned by orders of magnitude.

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.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this DP-900 question test?

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: Switch to Azure Synapse dedicated SQL pool with proper table partitioning — Azure Synapse Serverless SQL pool does not support partition elimination based on the partitioning of the underlying Parquet files in Azure Data Lake Storage Gen2. By switching to an Azure Synapse dedicated SQL pool with proper table partitioning on the date column, the query engine can perform partition pruning, scanning only the relevant partitions for the specified date range, which drastically reduces I/O and improves query performance to meet the sub-5-second requirement.

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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Last reviewed: Jun 11, 2026

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