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.
This AI Associate practice question tests your understanding of data for ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Which TWO are common data quality issues that can negatively impact AI model performance?
Missing values in critical fields
Missing data is a common quality issue.
Low model accuracy during validation
Why wrong: Accuracy is a model performance metric.
Insufficient storage space for data
Why wrong: Storage is infrastructure, not data quality.
Inconsistent data governance policies
Why wrong: Governance is about management, not quality.
Duplicate records in the dataset
Duplicates can skew the model.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Duplicate records (Option E) can cause overfitting or biased model training because the model effectively sees the same data point multiple times, inflating its importance in loss calculations. In practice, deduplication is often performed using hashing or record linkage techniques, and failure to do so can skew feature distributions and reduce generalization. For example, in a customer churn dataset, duplicate entries for the same customer can artificially amplify that customer's influence on the model's decision boundary.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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.)
easyWhy B: Options A and B are correct. Missing values and outliers can skew model training. Option C is wrong because high dimensionality is more about feature count than quality. Option D is wrong because multicollinearity affects interpretability but not necessarily quality. Option E is wrong because data volume alone is not a quality issue.
Last reviewed: Jun 30, 2026
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.
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