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.
Which TWO data preparation steps are critical for ensuring high-quality training data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Removing duplicate records.
Option B is correct because duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.
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.
✗
Increasing dataset size by adding noise.
Why it's wrong here
Adding noise without care can degrade data quality.
✓
Removing duplicate records.
Why this is correct
Duplicates can overrepresent certain patterns and skew model training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Normalizing all features.
Why it's wrong here
Normalization is important for some models but not always critical for data quality.
✓
Handling missing values appropriately.
Why this is correct
Missing values can cause errors or bias if not properly addressed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Using only labeled data.
Why it's wrong here
This depends on the problem; unlabeled data can be useful for unsupervised learning or semi-supervised approaches.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the distinction between data preparation steps that ensure data quality (like removing duplicates and handling missing values) versus optional preprocessing or augmentation techniques, leading candidates to mistakenly select normalization or noise addition as critical steps.
Detailed technical explanation
How to think about this question
In practice, duplicate detection often involves hashing or exact matching on key fields, but near-duplicates (e.g., slight variations in text or sensor readings) require more sophisticated techniques like locality-sensitive hashing or similarity thresholds. For example, in a customer dataset, duplicate records with slightly different spellings of the same name can skew a churn prediction model if not handled properly. Handling missing values appropriately (Option D) is also critical because ignoring them can lead to biased estimates or runtime errors, with common strategies including mean imputation, regression imputation, or using algorithms that support missing values natively.
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 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
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: Removing duplicate records. — Option B is correct because duplicate records in a dataset can cause the model to overfit to repeated patterns, biasing the learned distribution and reducing generalization. Removing duplicates ensures each data point contributes equally to training, which is essential for robust model performance.
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.
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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