DP-700 Ingest and Transform Data Practice Question
You are developing a Notebook in Microsoft Fabric to process high-volume sensor data. You need to optimize performance using Delta Lake features. Which TWO actions should you perform? (Choose two)
⚠ Common exam trap
Candidates often choose only one of the two options (Optimize or Z-Ordering), failing to realize that both are required to address small file issues and data skipping simultaneously.
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
✓
Enable Z-Ordering on frequently filtered columns
Optimizing Delta Lake tables involves managing file sizes and data distribution. Z-Ordering optimizes multi-column queries by co-locating related data, significantly reducing read amplification. Compaction (using OPTIMIZE) merges small files into larger, more efficient files, which directly improves scan performance. Implementing these features is essential for maintaining query speed as data grows in the Lakehouse, ensuring that the Spark engine spends less time scanning unnecessary file blocks during execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Z-Ordering on frequently filtered columns
Why this is correct
Z-Ordering is a technique to map multidimensional data to one dimension while preserving locality. By co-locating data in the same set of files, Spark can skip entire files during query execution, which dramatically reduces the amount of data read for range-based or point-lookup queries in large datasets.
- ✓
Execute the OPTIMIZE command on the target table
Why this is correct
The OPTIMIZE command performs file compaction, taking many small files and rewriting them into larger, optimized Parquet files. This reduces the metadata overhead and improves I/O performance during read operations, which is critical when processing streaming sensor data that generates numerous small files over time.
- ✗
Manually delete old Parquet files from the file system
Why it's wrong here
Manually deleting files from the storage account can corrupt the Delta table state. Delta Lake maintains a transaction log; bypassing this log will lead to inconsistencies or table errors. You must use the VACUUM command to safely remove expired files that are no longer referenced by the log.
- ✗
Convert the table to CSV format
Why it's wrong here
Converting to CSV is regressive. CSV files lack the metadata, schema enforcement, and transaction capabilities that Delta Lake provides. You would lose support for features like time travel, ACID transactions, and efficient partition pruning, leading to significantly worse performance and reliability for your high-volume sensor data processing.
- ✗
Increase the number of partitions to equal total file count
Why it's wrong here
Increasing partitions to match the file count causes an 'over-partitioning' problem. This leads to excessive small file creation and metadata overhead, which degrades performance rather than improving it. Partitioning should be based on logical query boundaries, not the raw number of files present in the storage.
About these practice questions
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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.