DA0-002 Data Analysis Practice Test 19 — 15 Questions

Question 1

A data analyst is creating a visualization to show the relationship between two continuous variables: advertising spend and sales revenue. The analyst wants to display the strength and direction of the linear relationship. Which type of chart should the analyst use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Scatter plot. A scatter plot is the appropriate visualization for examining the relationship between two continuous variables. It plots each observation as a point, allowing the analyst to see if there is a positive, negative, or no correlation, and to spot outliers. Bar charts, pie charts, and histograms are designed for categorical or univariate data and cannot effectively display bivariate relationships.

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Pie chart

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Bar chart

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Histogram

Question 2

A data analyst is working with a dataset that contains a column 'income' with a highly skewed distribution. The analyst wants to apply a transformation to make the distribution more symmetric for use in a linear regression model. Which transformation is most appropriate?

Accepted Answer

Logarithmic transformation. A logarithmic transformation is effective for reducing right skewness in positive data like income. It compresses the upper tail, making the distribution more symmetric and improving linearity for regression. Standardization and min-max normalization only rescale without changing shape. Binning discards information and does not achieve symmetry. Therefore, the logarithmic transformation is the correct choice.

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Min-max normalization

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Binning into equal-width intervals

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Standardization (z-score normalization)

Question 3

A data analyst is preparing a dataset for a machine learning model to predict customer churn. The dataset contains missing values in several columns. The analyst wants to handle missing data in a way that preserves as much information as possible and avoids introducing bias. Which TWO of the following techniques are appropriate? (Choose two.)

Accepted Answer

K-nearest neighbors (KNN) imputation. Multiple imputation by chained equations and KNN imputation are both advanced techniques that preserve data structure and reduce bias. MICE models each variable with missing data using other variables and creates multiple imputations to account for uncertainty. KNN imputation uses similarity between observations to estimate missing values. Both are superior to mean imputation, listwise deletion, or zero replacement, which either discard information or introduce bias.

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Listwise deletion

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Mean imputation for numerical features

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Replacing missing values with zero

Question 4

A data analyst is using a decision tree to classify loan applications as approved or denied. To avoid overfitting, the analyst plans to prune the tree. Which TWO of the following are valid pruning techniques? (Choose two.)

Accepted Answer

Post-pruning by removing branches that do not improve validation performance.. Pruning techniques aim to simplify a decision tree to reduce overfitting. Pre-pruning stops tree growth early, for example by limiting maximum depth. Post-pruning removes branches after the tree is fully grown, using validation performance to guide removal. Both are standard methods. Increasing features, adding data, or using ensembles are not pruning techniques for a single tree.

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Adding more training data to the dataset.

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Increasing the number of features used at each split.

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Using a random forest ensemble instead of a single decision tree.

Question 5

A data analyst is working with a dataset that contains a column 'region' with values such as 'North', 'South', 'East', 'West', and 'N/A'. The analyst needs to prepare this column for a machine learning model. Which of the following is the most appropriate approach to handle the 'N/A' values?

Accepted Answer

Treat 'N/A' as a separate category and encode it as its own level.. Treating 'N/A' as a separate category is the best approach because it retains the information that the region is missing or not applicable, which could be informative. Imputing with the mode or removing rows can introduce bias or lose data, and numeric encoding creates a false ordinal relationship.

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Replace 'N/A' with the mode of the 'region' column.

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Encode 'N/A' as a numeric value of 0 and other regions as 1-4.

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Remove all rows where 'region' is 'N/A'.

Question 6

A data analyst is reviewing a dataset and notices that the 'ProductCategory' column contains inconsistent entries such as 'Electronics', 'electronics', and 'ELECTRONICS'. Which data cleaning technique should the analyst apply to standardize these values?

Accepted Answer

Case normalization. Case normalization is the appropriate technique to standardize text values that differ only by case. By converting all entries to a consistent case, the analyst ensures that 'Electronics', 'electronics', and 'ELECTRONICS' are treated as the same category. Other techniques like normalization, standardization, and one-hot encoding are not designed to fix case inconsistencies in text data.

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Standardization

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Normalization

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One-hot encoding

Question 7

A data analyst is examining a dataset of employee salaries and notices that the distribution is heavily right-skewed due to a few executives with very high salaries. The analyst wants to apply a transformation to make the distribution more symmetric for further analysis. Which transformation is most appropriate?

Accepted Answer

Logarithmic transformation. For right-skewed data with positive values, the logarithmic transformation is the most appropriate to reduce skewness and make the distribution more symmetric. It compresses the upper tail, mitigating the influence of extreme high values. Square root is milder, while square and exponential transformations would increase skewness. Therefore, the log transformation is the best choice.

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Square root transformation

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Square transformation

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Exponential transformation

Question 8

A data analyst is using a decision tree to predict customer churn. The tree is very deep, with many branches, and achieves 99% accuracy on the training data but only 70% on the test data. Which technique should the analyst use to address this issue?

Accepted Answer

Apply pruning to the decision tree. Pruning is a technique specifically designed to reduce overfitting in decision trees by removing unnecessary branches. The large discrepancy between training and test accuracy indicates the model is too complex and has memorized the training data. Pruning simplifies the tree, improving its ability to generalize. Other options either worsen overfitting or do not directly address the model complexity.

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Use a larger training dataset

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Decrease the size of the test set

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Increase the maximum depth of the tree

Question 9

A data analyst is examining a dataset of customer transactions and notices that the distribution of transaction amounts is highly skewed to the right. The analyst wants to apply a transformation to make the distribution more symmetric for use in a linear regression model. Which of the following transformations is most appropriate?

Accepted Answer

Logarithmic transformation. For highly right-skewed positive data like transaction amounts, the logarithmic transformation is the standard choice to reduce skewness and make the distribution more symmetric. It compresses the upper tail and can linearize relationships, which benefits linear regression. Other transformations like square root are weaker, while reciprocal and squaring are either too strong or counterproductive.

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Box-Cox transformation with lambda = 2

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Square root transformation

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Reciprocal transformation

Question 10

A data analyst is validating a regression model that predicts monthly churn probability for 50,000 subscribers. The analyst wants to detect whether the model is overfitting before deploying it. (Choose two.)

Accepted Answer

Compare the model's error on the training set with its error on a held-out validation set. Overfitting is detected by measuring how well a model performs on data it did not train on. Holding out a validation set and comparing its error with training error reveals the generalization gap, while k-fold cross-validation repeats that comparison across multiple partitions to produce a stable estimate. Together they show whether the churn model has learned signal versus memorized noise, which is exactly what must be established before deployment.

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Remove all records with missing values from the validation set only

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Increase the number of predictor variables until training accuracy reaches 100 percent

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Apply a log transformation to the target churn probability before evaluation

Question 11

A data analyst at a subscription media company is asked to determine whether a new recommendation engine increased average watch time. The analyst plans a two-sample t-test comparing a randomly assigned control group against a randomly assigned treatment group, with watch time recorded in minutes per user per week. Which TWO conditions must hold for the pooled-variance two-sample t-test to be statistically valid here? (Choose two.)

Accepted Answer

The population variances must be equal, or the sample sizes must be large enough for the test's robustness.. The pooled two-sample t-test rests on independent observations and an assumption about variance equality that is relaxed by large, balanced samples. Random assignment to control and treatment satisfies independence, and checking variance similarity or group balance safeguards the test's nominal error rate. Sample-size thresholds and measurement-scale restrictions are not assumptions of this procedure, so they do not belong in the analyst's validation checklist.

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Both groups must contain at least 30,000 users to satisfy the central limit theorem.

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Watch time must be measured on a ratio scale with a true zero and no upper bound.

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The control and treatment groups must have identical means before the experiment begins.

Question 12

A data analyst receives a sales dataset where the 'order_date' column is stored as text in the format '2024-03-15'. The analyst needs to calculate the number of days between each order date and the current date. Which action should the analyst take first?

Accepted Answer

Convert the 'order_date' column from text to a date data type. Calculating elapsed days requires the order date to be a genuine date type so the platform can perform chronological subtraction. While the values look like dates, storing them as text means comparisons and arithmetic operate on character strings, producing errors or incorrect results. Converting the column to a date type is the prerequisite step that unlocks accurate day-difference calculations and correct date-based sorting.

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Encode the 'order_date' column using one-hot encoding

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Apply a logarithm transformation to the 'order_date' column values

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Impute missing values in the 'order_date' column with the column mean

Question 13

A data analyst is building a time series forecasting model for monthly sales data. The analyst observes that the data has a clear upward trend and a seasonal pattern that repeats every 12 months. After differencing the data once to remove the trend, the analyst notices that the seasonal pattern remains. Which of the following should the analyst do next to properly model the data?

Accepted Answer

Apply a seasonal difference with lag 12.. After removing the trend with non-seasonal differencing, the persistent seasonal pattern indicates the need for seasonal differencing. Taking a difference at lag 12 removes the yearly seasonal effect, making the series stationary in both trend and seasonality. This step is crucial before fitting a SARIMA model, which can then include seasonal autoregressive and moving average terms to capture the remaining structure.

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Include a moving average term of order 12.

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Fit a non-seasonal ARIMA model with only autoregressive terms.

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Apply a second round of non-seasonal differencing.

Question 14

A data analyst at a logistics company receives a shipment-tracking table in which the carrier identifier column contains values such as 'FEDX', 'FedEx', 'fed ex', and 'FX'. The analyst needs to consolidate these into a single carrier value before building a delivery-performance dashboard. Which data preparation step should the analyst perform?

Accepted Answer

Standardize and normalize the categorical values through data cleansing.. The carrier column suffers from inconsistent representation of the same entity, a classic data cleansing issue. Trimming whitespace, normalizing case, and mapping aliases to a canonical label collapses the variants into one category. Numeric rescaling, missing-value imputation, and one-hot encoding all address different problems and would leave the duplicated carrier categories intact in the delivery dashboard.

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One-hot encode the carrier column into binary indicator fields.

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Impute the missing carrier values using the column mean.

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Apply min-max normalization to rescale the carrier column.

Question 15

A data analyst is exploring a dataset of 8,000 customer transactions and notices that the 'transaction_amount' column has a mean of $120 but a median of $45. A small number of transactions exceed $10,000. Which measure of central tendency should the analyst report to describe the typical transaction?

Accepted Answer

The median, because it is resistant to the influence of extreme values. A large gap between the mean and median signals skew, and here the high-value transactions drag the mean well above the middle of the distribution. The median marks the point where half the transactions are smaller and half larger, so it reflects the typical customer experience far better. For skewed monetary data, the median is the more representative measure of central tendency.

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The range, because it shows the spread between the smallest and largest transactions

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The mean, because it uses every value in the dataset

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The mode, because it identifies the most frequently occurring transaction amount