UiPath-ADAv1 Files, Folders, and Data Manipulation Practice Question
Which data manipulation approach is most efficient for transforming a DataTable with 10,000 rows without using activities in a loop?
⚠ Common exam trap
Candidates often default to a 'For Each Row' loop because it is easier to write, ignoring that it is significantly slower than LINQ for large datasets.
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
✓
Use the 'Invoke Code' activity with a LINQ query.
Using LINQ on a DataTable allows for high-performance data transformation in memory. By leveraging 'AsEnumerable', you can perform complex filtering, sorting, and grouping operations without the overhead of UI-based activities. This approach is highly efficient for large datasets, significantly reducing the execution time compared to iterating row-by-row, as it utilizes optimized .NET underlying collection processing which is designed for speed and memory efficiency in enterprise-grade automation scenarios.
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 the 'For Each Row' activity to update values.
Why it's wrong here
The 'For Each Row' activity is slow for large datasets because it incurs overhead on every iteration, especially when combined with data manipulation activities. For 10,000 rows, this will significantly increase the total execution time compared to bulk-processing techniques like LINQ or DataTable methods that process the entire collection in one operation.
- ✓
Use the 'Invoke Code' activity with a LINQ query.
Why this is correct
Executing LINQ within 'Invoke Code' provides the highest performance for complex transformations. It allows the developer to use the full power of the .NET framework to manipulate DataTable objects in a single, memory-resident pass. This is the preferred method for large-scale data manipulation that requires high throughput and minimal latency in production.
- ✗
Write the data to Excel, perform the transformation there, and read it back.
Why it's wrong here
Externalizing transformation to Excel introduces significant latency due to the overhead of opening and manipulating the Excel process. This is inefficient for 10,000 rows and creates unnecessary dependencies on external software, which is prone to failure and slows down the automation significantly compared to native in-memory data processing techniques.
- ✗
Use the 'Filter Data Table' activity 10,000 times.
Why it's wrong here
Calling the 'Filter Data Table' activity repeatedly is architecturally flawed and extremely inefficient. Each call incurs design-time and runtime overhead, leading to massive performance degradation. Data should be processed in bulk using methods designed for aggregate operations rather than repetitive single-action calls which do not scale for high-volume automation tasks.
About these practice questions
This UiPath-ADAv1 question is part of Courseiva's 285-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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 UiPath exam blueprint
This UiPath-ADAv1 practice question is part of Courseiva's free UiPath 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 UiPath-ADAv1 exam.