DP-700 Ingest and Transform Data Practice Question
You are optimizing a Spark job in Fabric. Which TWO of the following techniques will help reduce the execution time? (Choose two)
⚠ Common exam trap
Candidates often select 'Partitioning' or 'Broadcasting' as the primary answer. While useful, they are not listed as the standard performance-tuning techniques compared to caching and adaptive query execution in this context.
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
✓
Cache frequently used DataFrames
Reducing execution time in Spark requires efficient resource utilization and minimizing unnecessary data processing. Caching DataFrames that are reused multiple times prevents redundant computations, while enabling adaptive query execution allows Spark to dynamically optimize the physical plan based on runtime statistics. Together, these strategies significantly lower latency and resource consumption, leading to a much faster and more cost-effective data transformation process within the Fabric Notebook environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cache frequently used DataFrames
Why this is correct
Caching intermediate results in memory prevents recomputing them from scratch when they are reused in subsequent transformations. This is a highly effective optimization technique for complex Spark jobs that perform multiple actions on the same dataset, reducing overall execution time and freeing up compute resources for other tasks.
- ✗
Always set the number of partitions to 1
Why it's wrong here
Setting partitions to 1 is a performance killer. It forces all data onto a single worker node, negating the benefits of parallel processing. This leads to severe bottlenecks and memory overflow errors, making it the wrong approach for scaling data processing in Spark-based cloud environments.
- ✓
Enable adaptive query execution
Why this is correct
Adaptive query execution allows Spark to optimize the query plan during runtime based on actual data characteristics. It can automatically coalesce small partitions and switch join strategies, which significantly improves performance without manual tuning. It is a critical feature for modern Spark workloads to achieve optimal throughput.
- ✗
Convert all data to JSON
Why it's wrong here
Converting to JSON is detrimental to performance. It increases serialization overhead and storage size compared to binary formats like Delta or Parquet. JSON is not an analytical format and will drastically increase the time spent on I/O and parsing during data processing jobs, making this an anti-pattern.
- ✗
Increase the idle timeout
Why it's wrong here
Increasing the idle timeout does not impact the execution time of a job. It only affects how long the compute resources remain active after the job finishes. While it might prevent startup delays for future jobs, it is not a technique to optimize the current job's processing logic.
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.