The answer is to investigate the data source to correct the negative values. This is the best course of action because negative revenue values are almost always a symptom of data entry errors, system bugs, or flawed data transformations, not a legitimate financial state. In data preparation for AI models, the core principle is data integrity—simply deleting or imputing these values without root-cause analysis can mask underlying system issues and introduce bias into your model. On the Salesforce AI Associate exam, this question tests your understanding of the data preparation phase, specifically that cleaning must begin with source validation before any statistical treatment. A common trap is to jump to imputation or deletion, but the exam emphasizes that the engineer’s first duty is to trace and fix the origin of the error. Memory tip: think “source first, fix the flow”—always correct bad data at its source before you clean the downstream dataset.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Investigate the data source to correct the negative values
Option D is correct because negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Delete all records with negative revenue
Why it's wrong here
Deleting records loses potentially valuable data.
✗
Replace negative values with the mean of positive values
Why it's wrong here
Imputation without investigation may introduce bias.
✗
Keep negative values as they might represent returns or refunds
Why it's wrong here
Negative revenue is unlikely and would confuse the model.
✓
Investigate the data source to correct the negative values
Why this is correct
Correcting at source ensures data integrity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that imputation (e.g., mean replacement) is a safe default for handling invalid data, when in fact the correct first step is always to trace and fix the root cause at the data source.
Trap categories for this question
Similar concept trap
Negative revenue is unlikely and would confuse the model.
Detailed technical explanation
How to think about this question
In data preparation, the 'garbage in, garbage out' principle means that any anomalies in raw data propagate through feature engineering and model training. For financial fields like 'AnnualRevenue', domain constraints (e.g., revenue >= 0) should be enforced, and any violation triggers a root-cause analysis—often involving SQL audits or ETL pipeline logs. A real-world scenario: a CRM system might incorrectly map a 'credit note' field to revenue, generating negative values; correcting the mapping at the source prevents recurring issues across all downstream models.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Investigate the data source to correct the negative values — Option D is correct because negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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