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DA0-001 · topic practice

Analyzing and Modeling Data practice questions

Practise CompTIA Data+ DA0-001 Analyzing and Modeling Data practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Analyzing and Modeling Data

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What the exam tests

What to know about Analyzing and Modeling Data

Analyzing and Modeling Data questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Analyzing and Modeling Data exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Analyzing and Modeling Data questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

Trap 1: Standard deviation

Standard deviation measures dispersion, not central tendency.

Trap 2: Median

Median is the middle value when sorted.

Trap 3: Mean

Mean is the average, not the most frequent value.

Study all Analyzing and Modeling Data common traps →

A
Mode
Mode is the most frequently occurring value.
B
Standard deviation
Why wrong: Standard deviation measures dispersion, not central tendency.
C
Median
Why wrong: Median is the middle value when sorted.
D
Mean
Why wrong: Mean is the average, not the most frequent value.

Full breakdown with real-world context →

Question 2mediummultiple choice

Read the full NAT/PAT explanation →

A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?

Trap 1: Linear regression

Linear regression does not inherently handle seasonality.

Trap 2: K-means clustering

Clustering is unsupervised and not for prediction.

Trap 3: Logistic regression

Logistic regression is for classification, not forecasting.

Study all Analyzing and Modeling Data common traps →

A
Linear regression
Why wrong: Linear regression does not inherently handle seasonality.
B
Time series analysis
Time series analysis explicitly models seasonal patterns.
C
K-means clustering
Why wrong: Clustering is unsupervised and not for prediction.
D
Logistic regression
Why wrong: Logistic regression is for classification, not forecasting.

Full breakdown with real-world context →

Question 3hardmultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

Trap 1: Accuracy

Accuracy is high (95%) but misleading due to imbalance.

Trap 2: F1 score

F1 combines precision and recall, but recall alone directly shows the issue.

Trap 3: Precision

Precision may be high if false positives are low, but it does not capture missed churners.

Study all Analyzing and Modeling Data common traps →

A
Accuracy
Why wrong: Accuracy is high (95%) but misleading due to imbalance.
B
F1 score
Why wrong: F1 combines precision and recall, but recall alone directly shows the issue.
C
Recall
Recall is low (10%), showing the model fails to detect churners.
D
Precision
Why wrong: Precision may be high if false positives are low, but it does not capture missed churners.

Full breakdown with real-world context →

Question 4easymultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

Trap 1: Concatenate

Concatenate can be ambiguous; in pandas, it can combine along rows or columns, but append is more specific.

Trap 2: Merge

Merge is for combining on a common key, not simply adding rows.

Trap 3: Aggregate

Aggregate summarizes data, not combine rows.

Study all Analyzing and Modeling Data common traps →

A
Concatenate
Why wrong: Concatenate can be ambiguous; in pandas, it can combine along rows or columns, but append is more specific.
B
Append
Append adds rows from one dataset to another with same columns.
C
Merge
Why wrong: Merge is for combining on a common key, not simply adding rows.
D
Aggregate
Why wrong: Aggregate summarizes data, not combine rows.

Full breakdown with real-world context →

Question 5mediummultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

Trap 1: Fail to reject the null hypothesis

p < alpha, so we reject null.

Trap 2: Accept the null hypothesis

We never accept null; we either reject or fail to reject.

Trap 3: The result is practically significant

Statistical significance does not imply practical significance.

Study all Analyzing and Modeling Data common traps →

A
Reject the null hypothesis
p < alpha indicates statistically significant result.
B
Fail to reject the null hypothesis
Why wrong: p < alpha, so we reject null.
C
Accept the null hypothesis
Why wrong: We never accept null; we either reject or fail to reject.
D
The result is practically significant
Why wrong: Statistical significance does not imply practical significance.

Full breakdown with real-world context →

Question 6hardmultiple choice

Read the full Analyzing and Modeling Data explanation →

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

Trap 1: Increase polynomial features

This would likely increase variance further.

Trap 2: Use a smaller training set

Smaller training set typically increases variance.

Trap 3: Remove correlated features

This may reduce variance but can increase bias; not the most direct method.

Study all Analyzing and Modeling Data common traps →

A
Apply Ridge regularization
Ridge adds penalty to coefficients, reducing overfitting and variance.
B
Increase polynomial features
Why wrong: This would likely increase variance further.
C
Use a smaller training set
Why wrong: Smaller training set typically increases variance.
D
Remove correlated features
Why wrong: This may reduce variance but can increase bias; not the most direct method.

Full breakdown with real-world context →

Question 7easymultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

Trap 1: Drop rows with missing values

Dropping rows may discard valuable data; imputation is preferred when appropriate.

Trap 2: Mean imputation

Mean is for numeric data.

Trap 3: Median imputation

Median is for numeric data.

Study all Analyzing and Modeling Data common traps →

A
Drop rows with missing values
Why wrong: Dropping rows may discard valuable data; imputation is preferred when appropriate.
B
Mode imputation
Mode is appropriate for categorical variables.
C
Mean imputation
Why wrong: Mean is for numeric data.
D
Median imputation
Why wrong: Median is for numeric data.

Full breakdown with real-world context →

Question 8mediummultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst needs to visualize the distribution of a continuous variable across different categories. Which chart type is most suitable?

Trap 1: Bar chart

Bar chart typically shows aggregate values, not distribution.

Trap 2: Histogram

Histogram shows distribution of one continuous variable without categories.

Trap 3: Scatter plot

Scatter plot is for two continuous variables.

Study all Analyzing and Modeling Data common traps →

A
Bar chart
Why wrong: Bar chart typically shows aggregate values, not distribution.
B
Histogram
Why wrong: Histogram shows distribution of one continuous variable without categories.
C
Scatter plot
Why wrong: Scatter plot is for two continuous variables.
D
Box plot
Box plot displays distribution across groups.

Full breakdown with real-world context →

Question 9hardmultiple choice

Read the full Analyzing and Modeling Data explanation →

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

Trap 1: Use accuracy as the evaluation metric

Accuracy is misleading for imbalanced data; F1 or AUC are better.

Trap 2: Undersample the majority class

Undersampling can help but may discard useful data; SMOTE is more effective.

Trap 3: Oversample the majority class

This would increase imbalance further.

Study all Analyzing and Modeling Data common traps →

A
Use accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data; F1 or AUC are better.
B
Undersample the majority class
Why wrong: Undersampling can help but may discard useful data; SMOTE is more effective.
C
Oversample the majority class
Why wrong: This would increase imbalance further.
D
Use SMOTE
SMOTE generates synthetic minority samples to balance classes.

Full breakdown with real-world context →

Question 10mediummulti select

Read the full Analyzing and Modeling Data explanation →

Which TWO of the following are common assumptions of linear regression?

Trap 1: Independence of observations

Independence is assumed, but it is not listed among the options as correct; however, the question asks for TWO, and B and D are the most commonly cited assumptions.

Trap 2: No multicollinearity

Multicollinearity is not an assumption; it is a condition that can be problematic.

Trap 3: Normality of the dependent variable

Normality of errors is assumed for inference, not the dependent variable.

Study all Analyzing and Modeling Data common traps →

A
Independence of observations
Why wrong: Independence is assumed, but it is not listed among the options as correct; however, the question asks for TWO, and B and D are the most commonly cited assumptions.
B
No multicollinearity
Why wrong: Multicollinearity is not an assumption; it is a condition that can be problematic.
C
Linearity of the relationship
Assumes linear relationship between predictors and outcome.
D
Normality of the dependent variable
Why wrong: Normality of errors is assumed for inference, not the dependent variable.
E
Homoscedasticity
Assumes constant variance of residuals.

Full breakdown with real-world context →

Question 11hardmulti select

Read the full Analyzing and Modeling Data explanation →

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Trap 1: Binning continuous variables

Binning is a discretization method, not specifically for outlier handling.

Trap 2: Imputing outliers with the mean

Mean is sensitive to outliers; median is better.

Study all Analyzing and Modeling Data common traps →

A
Transforming the data using log transformation
Transformation can reduce the impact of outliers.
B
Removing the outlier records
Removal is acceptable if outliers are errors or not representative.
C
Capping the outlier values at a certain percentile
Capping limits extreme values.
D
Binning continuous variables
Why wrong: Binning is a discretization method, not specifically for outlier handling.
E
Imputing outliers with the mean
Why wrong: Mean is sensitive to outliers; median is better.

Full breakdown with real-world context →

Question 12easymulti select

Read the full Analyzing and Modeling Data explanation →

Which TWO of the following are examples of supervised learning algorithms?

Trap 1: K-means clustering

Unsupervised learning.

Trap 2: Principal component analysis (PCA)

Unsupervised dimensionality reduction.

Trap 3: Apriori algorithm

Unsupervised association rule mining.

Study all Analyzing and Modeling Data common traps →

A
Linear regression
Supervised regression algorithm.
B
K-means clustering
Why wrong: Unsupervised learning.
C
Principal component analysis (PCA)
Why wrong: Unsupervised dimensionality reduction.
D
Decision trees
Supervised classification/regression.
E
Apriori algorithm
Why wrong: Unsupervised association rule mining.

Full breakdown with real-world context →

Question 13hardmultiple choice

Read the full NAT/PAT explanation →

A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?

Trap 1: Remove poorly calibrated predictions by discarding all patients…

Discarding data reduces sample size and may introduce bias.

Trap 2: Ignore calibration because AUC is the only metric that matters for…

Calibration is important for decision-making; AUC alone can be misleading.

Trap 3: Switch to a random forest model, which inherently produces…

Random forests often have better calibration but may not necessarily preserve AUC; also, this changes the model entirely.

Study all Analyzing and Modeling Data common traps →

A
Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.
Why wrong: Discarding data reduces sample size and may introduce bias.
B
Ignore calibration because AUC is the only metric that matters for readmission risk models.
Why wrong: Calibration is important for decision-making; AUC alone can be misleading.
C
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Platt scaling is designed to improve calibration while maintaining AUC.
D
Switch to a random forest model, which inherently produces better-calibrated probabilities.
Why wrong: Random forests often have better calibration but may not necessarily preserve AUC; also, this changes the model entirely.

Full breakdown with real-world context →

Question 14mediummultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst at a marketing firm is tasked with segmenting customers based on their purchasing behavior. The dataset contains 10,000 customers with features such as annual spend, frequency of purchases, recency of last purchase, and average order value. The analyst decides to use k-means clustering. After standardizing the features, the analyst runs k-means with k=3, k=4, and k=5, and computes the silhouette score for each: k=3: 0.45, k=4: 0.52, k=5: 0.48. The analyst also plots the elbow curve and observes that the within-cluster sum of squares (WCSS) decreases sharply from k=2 to k=4, then levels off. Based on these results, what is the most appropriate number of clusters?

Trap 1: k=2

Not evaluated, but elbow suggests k=4 is better.

Trap 2: k=3

Silhouette score is lower than k=4.

Trap 3: k=5

Silhouette score is lower than k=4.

Study all Analyzing and Modeling Data common traps →

A
k=4
Highest silhouette score and elbow point.
B
k=2
Why wrong: Not evaluated, but elbow suggests k=4 is better.
C
k=3
Why wrong: Silhouette score is lower than k=4.
D
k=5
Why wrong: Silhouette score is lower than k=4.

Full breakdown with real-world context →

Question 15easymulti select

Read the full Analyzing and Modeling Data explanation →

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

Trap 1: Durbin-Watson test for autocorrelation

Autocorrelation is not a core assumption for linear regression.

Trap 2: Variance inflation factor (VIF) for multicollinearity

Multicollinearity is a concern but not a core assumption.

Trap 3: Cook's distance to identify influential points

Influential points are diagnostics but not assumptions.

Study all Analyzing and Modeling Data common traps →

A
Durbin-Watson test for autocorrelation
Why wrong: Autocorrelation is not a core assumption for linear regression.
B
Q-Q plot to assess normality of residuals
Q-Q plot checks normality assumption.
C
Variance inflation factor (VIF) for multicollinearity
Why wrong: Multicollinearity is a concern but not a core assumption.
D
Cook's distance to identify influential points
Why wrong: Influential points are diagnostics but not assumptions.
E
Residual plots to check for homoscedasticity
Residual plots help verify constant variance.

Full breakdown with real-world context →

Question 16hardmultiple choice

Read the full Analyzing and Modeling Data explanation →

A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?

Network Topology

Trap 1: Duplicate customer IDs

All IDs appear unique.

Trap 2: Missing values in total_charges

No missing values are present.

Trap 3: Outliers in monthly_charges

Values like 99.65 are not extreme outliers.

Study all Analyzing and Modeling Data common traps →

A
Duplicate customer IDs
Why wrong: All IDs appear unique.
B
Missing values in total_charges
Why wrong: No missing values are present.
C
Inconsistent data in total_charges
The total_charges for the first customer is equal to monthly_charges, suggesting a calculation error.
D
Outliers in monthly_charges
Why wrong: Values like 99.65 are not extreme outliers.

Full breakdown with real-world context →

Question 17mediummultiple choice

Read the full NAT/PAT explanation →

A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?

Trap 1: Remove features with low variance to reduce noise

Low variance features are not the primary issue; the model is not learning from the minority class.

Trap 2: Use accuracy as the evaluation metric to monitor improvement

Accuracy is misleading for imbalanced data; precision-recall or AUC should be used.

Trap 3: Switch to a random forest model with default settings

Without handling imbalance, random forest may still predict majority class.

Study all Analyzing and Modeling Data common traps →

A
Remove features with low variance to reduce noise
Why wrong: Low variance features are not the primary issue; the model is not learning from the minority class.
B
Apply SMOTE to oversample the readmission class
SMOTE generates synthetic samples, balancing the classes and allowing the model to learn from the minority class.
C
Use accuracy as the evaluation metric to monitor improvement
Why wrong: Accuracy is misleading for imbalanced data; precision-recall or AUC should be used.
D
Switch to a random forest model with default settings
Why wrong: Without handling imbalance, random forest may still predict majority class.

Full breakdown with real-world context →

Question 18mediumdrag order

Read the full Analyzing and Modeling Data explanation →

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 19mediumdrag order

Read the full Analyzing and Modeling Data explanation →

Drag and drop the steps to implement a data classification policy in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 20mediummatching

Read the full Analyzing and Modeling Data explanation →

Match each data governance role to its responsibility.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Ensures data quality and adherence to policies

Manages technical environment and data access

Has accountability for specific data assets

Sets strategic direction for data management

Designs data structures and integration processes

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Frequently asked questions

What does the DA0-001 exam test about Analyzing and Modeling Data?: Analyzing and Modeling Data questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?: Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Analyzing and Modeling Data questions in a focused session?: Yes — the session launcher on this page draws every question from the Analyzing and Modeling Data domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other DA0-001 topics?: Use the topic links above to move to related areas, or go back to the DA0-001 question bank to see all topics.
Are these real exam questions or dumps?: These are original practice questions written to test the same concepts the DA0-001 exam covers. They are not copied from any real exam or dump site.

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All DA0-001 questions Analyzing and Modeling Data domain overview DA0-001 exam guide

Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Analyzing and Modeling Data practice questions

What to know about Analyzing and Modeling Data

Common Analyzing and Modeling Data exam traps

Analyzing and Modeling Data questions

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

A data analyst needs to visualize the distribution of a continuous variable across different categories. Which chart type is most suitable?

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

Which TWO of the following are common assumptions of linear regression?

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Which TWO of the following are examples of supervised learning algorithms?

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

Drag and drop the steps to implement a data classification policy in the correct order.

Match each data governance role to its responsibility.

Track your progress over time

Start a Analyzing and Modeling Data only practice session

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