DP-700 Monitor and Optimize an Analytics Solution Practice Question
Exhibit
{
"operation": "Optimize",
"table": "SalesData",
"action": "ZORDER",
"columns": ["RegionID", "DateKey"],
"status": "Failed",
"errorCode": "InsufficientMemory"
}Refer to the exhibit. You are attempting to run an OPTIMIZE command with Z-ORDER on a large Lakehouse table. The operation fails with an InsufficientMemory error. What should you do to resolve this?
⚠ Common exam trap
Candidates often attempt to reduce the dataset size or change the Z-ORDER column, which does not address the fundamental memory constraint occurring during the sorting process itself.
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
✓
Increase the executor memory configuration for the Spark pool.
The InsufficientMemory error during a Z-ORDER operation indicates that the Spark executor does not have enough memory to sort the data effectively during the optimization process. By increasing the executor memory, you provide the Spark engine with the necessary headroom to handle the shuffle required for Z-ORDER. This is a common requirement when processing large datasets, as Z-ORDER is a memory-intensive operation that involves sorting large data segments to improve query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of columns in the Z-ORDER clause.
Why it's wrong here
Reducing columns might slightly lower memory pressure, but the core issue is the overall memory allocated for the Spark shuffle operation. If the data volume remains high, the operation will likely continue to fail. Increasing executor memory is the standard, more reliable solution for memory-related Spark failures.
- ✓
Increase the executor memory configuration for the Spark pool.
Why this is correct
Z-ORDER operations require significant memory for sorting and shuffling data to align it by the specified columns. Increasing the executor memory provides the Spark engine with sufficient capacity to perform these intensive operations without exceeding the memory limits allocated to each node in the cluster.
- ✗
Delete existing files from the table directory.
Why it's wrong here
Manually deleting files in the Delta table directory will corrupt the Delta log and lead to data inconsistency. The OPTIMIZE command is designed to manage file cleanup automatically. You should never interfere with the physical file layer of a Delta table using manual file system deletions.
- ✗
Switch the table format to Parquet.
Why it's wrong here
Z-ORDER is a feature specific to Delta Lake tables. Switching to standard Parquet files would remove the ability to use Z-ORDER entirely, negating the performance benefits of optimized data skipping. Furthermore, this would require a complete rewrite of the data, which is not a feasible resolution.
Visual reference
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-700 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-700 exam.