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.
Exhibit
Total records: 10000
Missing values: 500
Duplicates: 200
Outliers in Amount: 50
Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?
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.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the 200 duplicate records
Option B is correct because duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.
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.
✗
Impute the 500 missing values with the mean
Why it's wrong here
Imputation is important but duplicates are a more obvious issue.
✓
Remove the 200 duplicate records
Why this is correct
Duplicates can artificially inflate certain patterns and cause data leakage.
Clue confirmation
The clue words "best", "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove the 50 outliers in the Amount field
Why it's wrong here
Outliers are not necessarily harmful and may contain signal.
✗
Skip all preprocessing and train the model directly
Why it's wrong here
Ignoring data quality degrades model performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the order of preprocessing steps, trapping candidates who jump to imputation or outlier removal without first cleaning duplicates, which is the foundational step in data preparation.
Detailed technical explanation
How to think about this question
Duplicate detection often relies on hashing or row-wise comparison across all columns; in pandas, `duplicated()` identifies exact duplicates, but near-duplicates may require fuzzy matching. Removing duplicates first ensures that subsequent statistics (mean, variance) are not artificially inflated, which is critical for techniques like mean imputation or z-score outlier detection. In real-world scenarios, duplicate records can arise from data entry errors, system glitches, or merge operations, and ignoring them can cause model evaluation metrics (e.g., accuracy) to be overly optimistic.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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: Remove the 200 duplicate records — Option B is correct because duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.
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", "first". 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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