Alteryx-Core Data Preparation Practice Question
Which TWO settings in the Data Cleansing tool handle missing values?
⚠ Common exam trap
Candidates often assume the Data Cleansing tool can automatically infer the best replacement for any data type, failing to distinguish between the specific settings for numeric vs. string field null handling.
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
✓
Replace Nulls with 0 (for numeric fields)
The Data Cleansing tool simplifies cleaning by offering preset options for common issues. For missing values, it allows you to replace nulls with zero for numbers or blank strings for text. This is a crucial step in data preparation, as downstream tools often fail or behave unpredictably when they encounter null values in numeric fields, necessitating a standardized replacement strategy early in the workflow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Replace Nulls with 0 (for numeric fields)
Why this is correct
This is a direct setting in the tool configuration designed to handle empty numeric cells. By converting nulls to 0, you ensure that mathematical calculations downstream do not return null results, which is essential for accurate financial or statistical reporting in Alteryx workflows where completeness is required.
- ✗
Replace Nulls with 1 (for numeric fields)
Why it's wrong here
The Data Cleansing tool does not have a default setting to replace nulls with 1. While a Formula tool could achieve this, the Cleansing tool is limited to 0 for numbers and empty strings for text, keeping the interface simple for the most common data preparation use cases.
- ✓
Replace Nulls with Blanks (for string fields)
Why this is correct
This option allows you to turn null text values into empty string entries. This is vital for string concatenation or text analysis where null values might cause errors or inconsistent results, ensuring that all string data is handled consistently throughout the transformation pipeline regardless of input quality.
- ✗
Replace Nulls with 'Unknown'
Why it's wrong here
The Data Cleansing tool does not support custom text replacements for nulls directly in its interface. It follows a strict, simplified design. If you need to replace nulls with a custom string, you must use a Formula tool with an IF-statement, which provides more control over the logic.
- ✗
Remove rows with Nulls
Why it's wrong here
The Data Cleansing tool does not remove rows containing nulls. To remove rows based on null values, you must use a Filter tool configured with the 'Is Not Null' condition. The Cleansing tool's scope is strictly confined to cell-level modifications rather than row-level deletion or filtering of your dataset.
About these practice questions
This Alteryx-Core question is part of Courseiva's 142-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 Alteryx exam blueprint
This Alteryx-Core practice question is part of Courseiva's free Alteryx 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 Alteryx-Core exam.