DA0-002 Practice Exam 59 — 20 Questions

Question 1

A data scientist runs a linear regression model to predict customer spending based on income. The R-squared value is 0.45 and the p-value for the slope coefficient is 0.03. At a significance level of α=0.05, which of the following conclusions is correct?

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

Accepted Answer

The slope is statistically significant, and the model explains 45% of the variance.. The p-value (0.03) is less than α (0.05), so the slope is statistically significant. R²=0.45 means the model explains 45% of the variance.

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The slope is not statistically significant, and the model explains 55% of the variance.

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The slope is statistically significant, and the model explains 55% of the variance.

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The slope is not statistically significant, and the model explains 45% of the variance.

Question 2

A data engineer is designing a system to store raw sensor data from thousands of IoT devices. The data is expected to be used for exploratory analytics and machine learning. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake is the most appropriate choice because it can store raw, unprocessed sensor data in its native format (e.g., JSON, Parquet, or binary) without requiring a predefined schema. This flexibility supports exploratory analytics and machine learning workflows where data schemas may evolve or be unknown at ingestion time. Data lakes also scale horizontally to handle the high volume and velocity of data from thousands of IoT devices, unlike traditional storage systems that impose rigid structures or size limits.

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Relational database

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Data mart

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Key-value store

Question 3

A data team needs to communicate insights about customer churn to the sales team. The insights include confidence intervals and p-values. The sales team is not familiar with statistics. Which of the following should the data team do?

Accepted Answer

Explain the practical implications of the results without statistical jargon.. The sales team lacks statistical background, so presenting confidence intervals and p-values directly would cause confusion. The data team should translate these results into practical business implications—such as 'customers with a 30-day inactivity are 40% more likely to churn'—without using terms like p-value or confidence interval. This aligns with the DA0-001 objective of tailoring communication to the audience's expertise level.

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Assume the sales team will learn the terms over time.

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Use technical terms but provide written definitions.

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Include a detailed statistical appendix.

Question 4

A retail company has merged with another firm and now needs to create a unified customer data warehouse. The existing systems use different data classification methods: System A stores customer income as a categorical range (e.g., '$0-$50k', '$50k-$100k', '$100k+') while System B stores exact income as a decimal number. A data analyst must combine these into a single table. The goal is to perform statistical analysis that includes calculating average income, but the categorical data from System A loses precision. The analyst proposes converting System B's exact values into the same ranges as System A to ensure consistency. However, the data governance team wants to preserve as much detail as possible. Which course of action should the analyst recommend?

Accepted Answer

Impute System A's categorical data with the midpoint of each range to create a continuous numeric field, then combine with System B's exact values. Imputing the midpoint of each income range converts System A's categorical data into a continuous numeric field, allowing it to be combined with System B's exact decimal values. This approach preserves the granularity of System B's data while enabling statistical calculations like average income across the unified dataset, balancing the data governance team's requirement for detail with the need for consistency.

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Store both columns separately and treat them as independent attributes

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Convert System B's exact income to ranges matching System A, then combine

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Use only System B's data and discard System A because it is less precise

Question 5

Which stage of the data lifecycle involves converting raw data into a usable format, such as cleaning or validating?

Accepted Answer

Processing. Processing is the stage where raw data is transformed into a usable format through cleaning, validation, normalization, or aggregation. This step ensures data quality and consistency before analysis or storage, directly matching the question's description.

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Archival

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Ingestion

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Storage

Question 6

In the data lifecycle, which phase involves converting raw data into a usable format for analysis?

Accepted Answer

Processing. The processing phase in the data lifecycle is specifically where raw data is cleaned, transformed, and structured into a usable format for analysis. This includes operations such as parsing, normalization, deduplication, and conversion into formats like Parquet or Avro, which are optimized for query engines like Apache Spark or Presto.

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Ingestion

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Analysis

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Archival

Question 7

Refer to the exhibit. Which conclusion can be drawn from this data quality report?

Accepted Answer

The Email_Address column has a high uniqueness rate but needs improvement in validity.. The data quality report shows that the Email_Address column has a high uniqueness rate (e.g., 100% unique values), indicating no duplicate entries, but a low validity score (e.g., many entries fail format checks like missing '@' or domain). This means the column is structurally unique but contains invalid data, so it needs improvement in validity.

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The column is fully consistent but has low completeness.

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The column has low validity and low uniqueness.

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The column requires immediate action to improve completeness.

Question 8

A data analyst needs to visualize the relationship between advertising spend and sales revenue for 50 products. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart type for visualizing the relationship between two continuous variables—advertising spend and sales revenue—across 50 products. It allows the data analyst to assess correlation, trends, and potential outliers by plotting each product as a point on a Cartesian plane, with advertising spend on the x-axis and sales revenue on the y-axis.

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Histogram

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Bar chart

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Pie chart

Question 9

A data analyst at a retail company is building a dashboard for store managers to track sales performance. The data comes from three sources: point-of-sale (POS) systems, inventory, and customer loyalty. The POS table contains columns transaction_id, store_id, date, product_id, quantity, and price. The inventory table has product_id, store_id, stock_level, and reorder_point. The loyalty table has customer_id, transaction_id, and points_earned. The analyst creates a star schema with a sales_fact fact table containing all rows from POS, dimension tables for store, product, date, and customer. To calculate average transaction value, the analyst uses the formula SUM(quantity * price) / COUNT(*). Store managers report that the average transaction value appears too low, especially for stores with multiple registers. The analyst realizes that because each product sold in a transaction creates a separate row in sales_fact, a single transaction with multiple items contributes multiple rows. The current calculation divides by the number of rows rather than the number of distinct transactions. Which of the following is the best course of action to correct the average transaction value metric? (Choose one.)

Accepted Answer

Create a calculated field that sums sales per transaction (quantity * price) and then averages across distinct transaction IDs. It first sums sales per transaction (quantity * price) across all rows of the same transaction, then averages those sums across distinct transaction IDs. This directly solves the problem of dividing by the number of rows (which overcounts) because each transaction now contributes exactly one value to the average. Option A (using MEDIAN) is incorrect because median does not produce the average and does not fix the row-counting issue. Option B is too vague—simply 'aggregating' without specifying the measure and grouping could still produce incorrect results, and it does not clearly describe the correct method of summing per transaction. Option C is unnecessary; the star schema is appropriate, and the error is in the calculation, not the data model.

Answer

Use the MEDIAN function instead of AVG

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Aggregate the data at the transaction level before calculating the average

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Use a different data model that denormalizes transaction totals into a new fact table

Question 10

A data analyst is working in Looker Studio and wants to add a metric that counts the number of unique customers per month. In the context of Looker Studio, which type of field is this metric?

Accepted Answer

Metric. In Looker Studio, a metric is a field that aggregates numeric data, such as counts, sums, or averages. Counting unique customers per month requires an aggregation (COUNT_DISTINCT) applied to a numeric or identifier field, which is the defining characteristic of a metric. Dimensions are used for grouping and categorizing data, not for performing calculations like counting unique values.

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Dimension

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Parameter

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Blend

Question 11

An analyst has a dataset with monthly sales figures for the past five years. They want to highlight the cumulative effect of sales over time and show the total contribution of each month stacked on top of previous months. Which chart type is most suitable?

Accepted Answer

Area chart. An area chart is the most suitable choice because it fills the space between the line and the baseline with color, visually emphasizing the cumulative total over time. This allows the analyst to see both the trend of monthly sales and the stacked contribution of each month to the overall cumulative effect, which is exactly what the question requires.

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Line chart

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Stacked bar chart

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Waterfall chart

Question 12

A data analyst wants to show the monthly sales trend for the past two years with an emphasis on cumulative growth. Which chart type is most appropriate?

Accepted Answer

Area chart. An area chart is the most appropriate choice because it combines a line chart's ability to show trends over time with a filled area beneath the line, which visually emphasizes cumulative growth. For monthly sales data over two years, the area fill makes it easy to see both the month-over-month trend and the total accumulated sales volume, which is exactly what the data analyst needs.

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Scatter plot

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Bar chart

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Line chart

Question 13

A data analyst wants to show the relationship between advertising spend and sales revenue for 50 stores. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart for showing the relationship between two continuous variables—advertising spend and sales revenue—across 50 stores. Each point on the plot represents one store, allowing the analyst to visually assess correlation, trends, or outliers. This aligns with the DA0-001 objective of selecting visualizations that best represent bivariate relationships.

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Line chart

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Bar chart

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Pie chart

Question 14

An analyst is choosing a chart to show the correlation between two continuous variables. Which TWO chart types could be used? (Select two.)

Accepted Answer

Bubble chart. A bubble chart is an extension of a scatter plot that can show the correlation between two continuous variables on the x- and y-axes, while a third variable is represented by the size of the bubbles. For the specific purpose of showing correlation between exactly two continuous variables, the bubble chart is valid because the bubble size is optional and does not interfere with the primary x-y relationship. This makes it a correct choice for visualizing the relationship between two continuous variables.

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Pie chart

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Waterfall chart

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Histogram

Question 15

A data analyst wants to retrieve data from a REST API that returns JSON. Which step is part of the data lifecycle for this activity?

Accepted Answer

Data ingestion. Ingestion is the process of bringing data into a system for further processing.

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Data archival

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Data sharing

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Data deletion

Question 16

A hospital wants to analyze patient readmission rates. The data contains daily patient visits. What is the level of granularity?

Accepted Answer

Visit. The level of granularity refers to the finest detail captured in the dataset. Since the data contains daily patient visits, each record represents a single visit event, not the patient or the day itself. Therefore, 'Visit' is the correct granularity because each row corresponds to one visit occurrence.

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Patient

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Day

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Hospital

Question 17

A retail company wants to forecast monthly sales for the next 12 months. Sales data shows a clear upward trend and seasonal patterns that repeat yearly. Which time series model is most appropriate?

Accepted Answer

Holt-Winters exponential smoothing. The Holt-Winters exponential smoothing model (option C) is the most appropriate because it explicitly captures both trend and seasonality components, which are present in the sales data (upward trend and yearly seasonal patterns). Unlike simple exponential smoothing, Holt-Winters includes additive or multiplicative seasonal terms, making it ideal for data with clear, repeating seasonal cycles over a 12-month horizon.

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SARIMA

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Simple exponential smoothing

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ARIMA

Question 18

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?

Accepted Answer

k=4. The silhouette score is highest at k=4 (0.52), indicating that clusters are well-separated and cohesive. The elbow curve shows WCSS decreasing sharply up to k=4 and then leveling off, suggesting that k=4 captures the optimal trade-off between model complexity and variance explained. Together, these metrics point to k=4 as the most appropriate number of clusters.

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k=2

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k=3

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k=5

Question 19

In multiple linear regression, which TWO assumptions are critical for unbiased coefficient estimates? (Choose two.)

Accepted Answer

Linearity: the relationship between predictors and response is linear. For unbiased coefficient estimates in multiple linear regression, the linearity assumption (A) ensures that the model correctly specifies the functional form, and the independence of errors assumption (E) ensures that errors are uncorrelated, both of which are required for ordinary least squares (OLS) estimates to be unbiased. Homoscedasticity (D) is not required for unbiasedness but for efficiency (Gauss-Markov theorem).

Answer

Large sample size

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Normality of errors

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Homoscedasticity: errors have constant variance

Question 20

While reviewing a dashboard, an analyst notices that the data in a trend line chart does not match the underlying data due to a filter setting. The dashboard is used for weekly executive meetings. What should the analyst do?

Accepted Answer

Document the issue and fix the filter before the next meeting.. The analyst has identified a data integrity issue caused by a filter setting that directly impacts the accuracy of the trend line chart. The dashboard is used for weekly executive meetings, so the analyst must document the discrepancy and correct the filter before the next meeting to ensure data-driven decisions are based on accurate visualizations. This aligns with best practices in data governance and the principle of maintaining trust in reporting tools.

Answer

Ignore the discrepancy if it is small.

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Wait for someone to complain before acting.

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Immediately remove the dashboard and send raw data.