DA0-002 Data Analysis Practice Test 18 — 15 Questions

Question 1

A data analyst is creating a visualization to show the proportion of total sales contributed by each of five product categories. The analyst wants to emphasize the relative size of each category as part of a whole. Which type of chart is most appropriate?

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

Accepted Answer

Pie chart. A pie chart is the most suitable because it visually represents each category as a slice of the whole, making it easy to compare proportions. Line charts, scatter plots, and histograms serve different purposes and do not effectively communicate part-to-whole relationships.

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Histogram

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Line chart

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Scatter plot

Question 2

A data analyst is building a decision tree to classify whether customers will churn. The analyst wants to prevent the tree from overfitting the training data. Which technique should the analyst use?

Accepted Answer

Pruning the tree after full growth. Pruning is a standard technique to reduce decision tree overfitting by removing branches that do not improve predictive accuracy on validation data. It simplifies the model and enhances generalization. Increasing depth, using all features, or allowing single-sample leaves all increase complexity and overfitting risk, making them incorrect choices for this scenario.

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Increasing the maximum depth of the tree

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Setting the minimum samples per leaf to 1

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Using all available features without selection

Question 3

A data analyst is working with a dataset that includes a categorical variable 'product_category' with 50 unique values. The analyst wants to reduce dimensionality before clustering. Which technique should the analyst use?

Accepted Answer

Multiple correspondence analysis (MCA). Multiple correspondence analysis is a dimensionality reduction technique tailored for categorical data. It converts categories into numerical dimensions that capture the underlying structure, making it suitable for clustering. Unlike one-hot encoding, it reduces rather than expands the feature space. PCA and factor analysis are designed for continuous data, so they are not directly applicable to a categorical variable with many levels.

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One-hot encoding

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Factor analysis

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Principal component analysis (PCA)

Question 4

A data analyst is working with a dataset that contains a column for 'customer satisfaction rating' on a scale from 1 to 5, where 1 is very dissatisfied and 5 is very satisfied. The analyst wants to summarize the central tendency of this data. Which measure of central tendency is most appropriate for this ordinal data?

Accepted Answer

Median. For ordinal data such as satisfaction ratings, the median is the most appropriate measure of central tendency because it does not assume equal intervals between categories. It identifies the middle value when the data is ordered, providing a robust summary that is not influenced by extreme ratings. The mean assumes equal intervals, and the mode may not represent the center, while the range measures spread.

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Range

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Mode

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Mean

Question 5

A data analyst is reviewing a dashboard that tracks monthly sales. The analyst notices that the 'Total Sales' metric for the current month is significantly lower than previous months, but the underlying daily sales data appears normal. The dashboard uses a live connection to the database. Which of the following is the most likely cause of the discrepancy?

Accepted Answer

The dashboard's date filter is set to include only the first week of the current month.. The most likely cause is a date filter limiting the data to a subset of the month, such as the first week, which would reduce the total while daily sales remain normal. Other options would affect the daily data or show different patterns, making them less consistent with the scenario.

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The database connection is using a stale cache that has not refreshed since last month.

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The 'Total Sales' metric is calculated using a moving average that lags by one month.

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The sales data for the current month has not been fully loaded into the database yet.

Question 6

A data analyst is examining a dataset of customer transactions and notices that the 'transaction_amount' column contains negative values. The analyst determines that these negative values represent refunds. Which data quality dimension is most directly relevant to this finding?

Accepted Answer

Validity. Validity is about whether data adheres to defined rules or constraints. Negative transaction amounts may be valid if refunds are permitted, but the analyst must verify that such values are allowed by business rules. Accuracy, completeness, and consistency do not directly address the conformity to rules. Thus, validity is the most relevant dimension when encountering unexpected negative values that might indicate either a data error or a legitimate business process.

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Consistency

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Accuracy

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Completeness

Question 7

A data analyst is examining the relationship between advertising spend and sales revenue across 50 regions. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following conclusions is most appropriate?

Accepted Answer

There is a strong positive linear relationship between advertising spend and sales revenue.. A Pearson correlation of 0.85 indicates a strong positive linear relationship between the two variables. It does not establish causation, nor does it directly state the proportion of variance explained. The correlation coefficient must be squared to get the coefficient of determination, and significance requires a hypothesis test. Thus, the appropriate conclusion is the strong positive linear association.

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Increasing advertising spend causes an increase in sales revenue.

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The relationship is not statistically significant because the correlation is less than 0.90.

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Advertising spend explains 85% of the variation in sales revenue.

Question 8

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.)

Accepted Answer

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.

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Imputation should always use the mean for numerical variables to preserve the distribution.

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Imputation should be performed before splitting data into training and test sets to avoid data leakage.

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The choice of imputation method should be based solely on computational efficiency.

Question 9

A data analyst is working with a dataset that contains a column for 'order_date' stored as text in the format 'YYYY-MM-DD'. The analyst needs to calculate the average time between orders for each customer. Which of the following steps should the analyst take first?

Accepted Answer

Convert the 'order_date' column to a date data type using a function like TO_DATE or PARSE_DATE.. To calculate the average time between orders, the analyst must first convert the text-based date column into a proper date data type. This enables the use of date functions to compute intervals between consecutive orders. Without this conversion, any arithmetic on dates would be invalid or highly error-prone. Once converted, the analyst can sort by customer and date, then compute differences.

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Calculate the difference between consecutive 'order_date' values using string manipulation.

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Group the data by customer and compute the average of the 'order_date' column.

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Sort the dataset by the 'order_date' column in ascending order.

Question 10

A data analyst is building a predictive model to forecast customer churn. The dataset includes a categorical feature 'payment_method' with categories: credit card, debit card, PayPal, and bank transfer. The analyst decides to use one-hot encoding. After encoding, the analyst notices that the model's performance on the training set is excellent but poor on the test set. Which issue is most likely contributing to this problem?

Accepted Answer

The model is overfitting due to high dimensionality from one-hot encoding combined with a small dataset.. Overfitting is indicated by strong training performance and weak test performance. One-hot encoding expands the feature space, and with a small dataset, this can lead to a model that memorizes training noise. The other options do not directly explain overfitting: multicollinearity affects interpretation, data leakage would inflate both training and test scores, and payment methods are nominal so ordinality is irrelevant. Thus, high dimensionality from encoding is the most plausible cause.

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The one-hot encoding caused data leakage from the test set into the training set.

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The one-hot encoding failed to capture the ordinal relationship among payment methods.

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The one-hot encoding introduced multicollinearity among the payment method dummy variables.

Question 11

A data analyst is evaluating a classification model for predicting customer churn. The model's confusion matrix shows 100 true positives, 20 false positives, 30 false negatives, and 850 true negatives. The analyst wants to assess the model's ability to correctly identify actual churners. Which metric should the analyst use?

Accepted Answer

Recall. The analyst wants to assess the model's ability to correctly identify actual churners, which is the definition of recall (sensitivity). Recall is calculated as true positives divided by the sum of true positives and false negatives. In this scenario, recall is 100/(100+30) ≈ 0.769. Precision and specificity focus on different aspects, and accuracy can be misleading in imbalanced datasets.

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Precision

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Accuracy

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Specificity

Question 12

A data analyst is evaluating a classification model that predicts customer churn. The model has an accuracy of 95%, but the analyst suspects it is not performing well on the minority class (churners). The dataset is highly imbalanced, with only 5% churners. Which metric should the analyst prioritize to assess the model's ability to correctly identify churners?

Accepted Answer

Recall. Recall is the appropriate metric because it quantifies how many actual churners the model successfully identifies. In imbalanced settings, accuracy and specificity can be high even when the model fails to detect the minority class. Precision is also important but secondary when the goal is to capture as many churners as possible.

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Specificity

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Accuracy

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Precision

Question 13

A data analyst is analyzing a time series of monthly sales for a retail store. The analyst observes a repeating pattern every 12 months. Which component of the time series is the analyst observing?

Accepted Answer

Seasonality. Seasonality is the time series component that captures repeating patterns at fixed intervals, such as every 12 months in monthly data. The analyst's observation of a yearly repeating pattern in sales is a direct example of seasonality. Trend, cyclicality, and noise do not describe fixed-period repetitions. Therefore, seasonality is the correct component.

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Noise

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Trend

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Cyclicality

Question 14

A data analyst is working with a dataset containing a categorical variable 'Education Level' with categories: High School, Bachelor's, Master's, PhD. The analyst wants to include this variable in a regression model. Which encoding technique should the analyst use?

Accepted Answer

One-hot encoding. One-hot encoding is the preferred method for nominal categorical variables in regression because it avoids imposing an artificial order or equal spacing. It creates separate binary indicators, allowing the model to estimate distinct effects for each education level. Label, binary, and ordinal encoding all introduce numerical relationships that may misrepresent the categorical nature of the data.

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Binary encoding

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Label encoding

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Ordinal encoding

Question 15

A data analyst is evaluating a classification model that predicts whether a customer will churn. The model's confusion matrix shows 80 true positives, 20 false negatives, 30 false positives, and 120 true negatives. Which TWO of the following metrics can be directly calculated from this confusion matrix? (Choose two.)

Accepted Answer

Accuracy. Accuracy and precision are both classification metrics that can be computed directly from the counts in a confusion matrix. Accuracy gives the overall correct prediction rate, while precision focuses on the reliability of positive predictions. R-squared, RMSE, and adjusted R-squared are regression metrics and cannot be calculated from a confusion matrix, so they are not applicable.

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R-squared

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Root Mean Square Error (RMSE)

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Adjusted R-squared