DA0-002 Data Analysis Practice Question
A data analyst is preparing a dataset for a machine learning model and needs to handle missing values in several columns. The analyst wants to choose appropriate imputation methods. Which TWO of the following are valid considerations when selecting an imputation technique? (Choose two.)
⚠ Common exam trap
The trap here is assuming that mean imputation is always safe and that imputation can be done before splitting; both can lead to biased models.
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
✓
The proportion of missing data in a column affects the reliability of imputation.
The missing data mechanism determines whether imputation can be unbiased, and the proportion of missing data affects reliability. These are key statistical considerations. Mean imputation is not always appropriate, imputation should occur after train-test split to avoid leakage, and efficiency alone is insufficient. Therefore, the mechanism and proportion are the valid considerations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Imputation should always use the mean for numerical variables to preserve the distribution.
Why it's wrong here
Using the mean is a common but not always appropriate method. It reduces variance and can distort relationships with other variables. For skewed distributions or when data is not MCAR, mean imputation can introduce bias. Therefore, it is not a universally valid consideration; the choice depends on the data and context.
- ✓
The proportion of missing data in a column affects the reliability of imputation.
Why this is correct
A high proportion of missing values (e.g., >50%) can make imputation unreliable and may warrant dropping the column or using advanced techniques. The amount of missingness impacts the confidence in imputed values and the potential for bias. Thus, it is a valid consideration when selecting an imputation method.
- ✓
The missing data mechanism (MCAR, MAR, MNAR) influences the choice of imputation method.
Why this is correct
Understanding whether data is Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR) is crucial because it determines whether simple imputation (e.g., mean) is unbiased. For MNAR, more complex methods like multiple imputation or model-based approaches are needed to avoid bias. Thus, this is a valid consideration.
- ✗
Imputation should be performed before splitting data into training and test sets to avoid data leakage.
Why it's wrong here
Imputation should be done after splitting to prevent data leakage from the test set into the training process. Calculating imputation parameters (e.g., mean) on the entire dataset would leak information. Therefore, this statement is incorrect; imputation must be fit on training data and applied to test data.
- ✗
The choice of imputation method should be based solely on computational efficiency.
Why it's wrong here
While efficiency matters for large datasets, the primary considerations are statistical validity and the nature of the missing data. Choosing solely for speed can lead to biased or misleading results. Thus, this is not a valid consideration; accuracy and appropriateness should guide the choice.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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