AI Associate Data for AI Practice Question
Which TWO of the following are common dimensions of data quality that must be addressed for AI training?
⚠ Common exam trap
Salesforce often tests the distinction between data quality dimensions (accuracy, completeness, consistency) and operational or security attributes (storage efficiency, encryption strength), tricking candidates into selecting options that sound technical but are irrelevant to data quality for AI training.
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
✓
Accuracy of values
Accuracy of values (Option B) is a fundamental dimension of data quality because AI models learn patterns from training data; if the data contains incorrect values, the model will learn and propagate those errors, leading to unreliable predictions. For example, in a dataset of customer ages, a single erroneous entry of '200' can skew the model's understanding of age distributions, directly impacting model 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.
- ✗
Storage efficiency
Why it's wrong here
Storage efficiency is a performance concern.
- ✓
Accuracy of values
Why this is correct
Accuracy ensures data correctly represents real-world entities.
- ✗
Encryption strength
Why it's wrong here
Encryption is a security feature, not a quality dimension.
- ✗
Consistency with external benchmarks
Why it's wrong here
Not a standard data quality dimension.
- ✓
Completeness of records
Why this is correct
Completeness measures if all necessary data is present.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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