Common Data Quality Issues That Negatively Impact AI Model Performance
Which TWO are common data quality issues that can negatively impact AI model performance?
Quick Answer
The answer is duplicate records in the dataset and missing values in critical fields. Duplicate records distort the model’s perception of data distribution, causing it to overemphasize repeated patterns and leading to biased predictions, while missing values in critical fields force models that rely on gradient-based optimization to either fail or learn skewed relationships if left unaddressed. On the Salesforce AI Associate exam, this question tests your understanding of data preparation fundamentals, often appearing as a multi-select trap where candidates confuse data quality issues with model tuning problems—remember that duplicates and missing values are input-side flaws, not algorithm errors. A helpful memory tip is “Dupes and Nulls, the model’s dulls,” as both issues dull the model’s ability to generalize accurately.
⚠ Common exam trap
Salesforce often tests the distinction between data quality issues (problems with the data itself) and model performance issues or infrastructure constraints, so candidates mistakenly select options like low accuracy or insufficient storage as data quality problems.
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
✓
Missing values in critical fields
Missing values in critical fields (Option A) are a common data quality issue because many AI models, particularly those relying on statistical or gradient-based optimization, cannot handle null or NaN inputs without imputation or removal. If missing values are not addressed, the model may learn biased patterns or fail to converge, leading to degraded predictive performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Missing values in critical fields
Why this is correct
Missing data is a common quality issue.
- ✗
Low model accuracy during validation
Why it's wrong here
Accuracy is a model performance metric.
- ✗
Insufficient storage space for data
Why it's wrong here
Storage is infrastructure, not data quality.
- ✗
Inconsistent data governance policies
Why it's wrong here
Governance is about management, not quality.
- ✓
Duplicate records in the dataset
Why this is correct
Duplicates can skew the model.
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Same concept, more angles
1 more way this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO are common data quality issues that negatively impact AI model performance? (Choose two.)
easy- A.Multicollinearity
- ✓ B.Missing values
- ✓ C.Outliers
- D.Data volume
- E.High dimensionality
Why B: Both missing values and outliers are common data quality issues that can negatively impact AI model performance. Missing values can lead to biased predictions or cause algorithms to fail, while outliers can skew the model's training, leading to poor generalization. Options A, D, and E are not primarily data quality issues: multicollinearity affects interpretability but not necessarily quality, data volume alone is not a quality issue, and high dimensionality is more about feature count than data quality.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.