DA0-002 Practice Test 16 — 15 Questions

Question 1

Which TWO are common methods for acquiring internal data? (Choose two.)

Accepted Answer

Transaction logs. Transaction logs (B) are a common internal data source because they are generated by an organization's own systems, capturing events such as purchases, clicks, or system activity within the company's operational environment. ERP systems (D) are also a core internal data source, since they store and manage enterprise operational data like finance, inventory, HR, and supply chain records generated inside the organization. By contrast, social media APIs (A), government databases (C), and web scraping (E) are external data acquisition methods, as they pull data from sources outside the organization's direct control.

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Social media APIs

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Government databases

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Web scraping

Question 2

A data analyst is performing data profiling on a customer table. Which metric provides the number of unique values in a column?

Accepted Answer

Cardinality. Cardinality refers to the number of distinct (unique) values in a column, which is exactly the metric described. It is a fundamental data profiling statistic used to assess column uniqueness, identify candidate keys, and inform indexing or partitioning decisions. High cardinality columns (e.g., primary keys) have many unique values; low cardinality columns (e.g., status flags) have few.

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Row count

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Standard deviation

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Null count

Question 3

A data analyst needs to visualize the distribution of salaries across departments and also compare the median and identify outliers. Which chart type is most suitable?

Accepted Answer

Box plot. A box plot is purpose-built for exactly this task: it displays the five-number summary (minimum, Q1, median, Q3, maximum) and plots points beyond 1.5×IQR as outliers. This lets an analyst compare medians and spread across departments side-by-side while visually flagging outliers in one view.

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Histogram

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Bar chart

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Scatter plot

Question 4

A data analyst is conducting exploratory data analysis (EDA) on a dataset. Which TWO tasks are typically performed during EDA? (Select two.)

Accepted Answer

Identify outliers using the IQR method. Option D is correct because identifying outliers with the IQR method is a core EDA activity: you compute Q1 and Q3, derive IQR = Q3 − Q1, and flag values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR to understand data quality and distribution. Option E is correct because calculating correlations (e.g., Pearson's r for linear relationships or Spearman's rank for monotonic ones) between variables is a standard EDA step to reveal associations and guide feature selection. Option A is not an EDA task; a sampling plan belongs to study/survey design and data collection planning, which precedes analysis. Option B is predictive modeling, a confirmatory phase that comes after EDA rather than during it. Option C is model deployment, an MLOps/production activity that occurs long after EDA.

Answer

Create a sampling plan

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Build a predictive regression model

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Deploy the model to production

Question 5

A data analyst needs to visualize the relationship between two continuous variables: advertising spend (in dollars) and monthly sales (in units). Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart for visualizing the relationship between two continuous variables, such as advertising spend (dollars) and monthly sales (units). It displays individual data points on an x-y axis, allowing the analyst to observe correlation, clustering, or outliers between the two numeric fields. This aligns with the DA0-001 objective of selecting visualizations based on data type and analytical goal.

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Pie chart

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Bar chart

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Line chart

Question 6

A stakeholder asks for the exact number of customers who churned last month. Which metric should the analyst report?

Accepted Answer

Count of churned customers. The stakeholder explicitly asks for the 'exact number' of customers who churned, which is a discrete count. Option C, 'Count of churned customers,' directly provides this integer value without any normalization or ratio. The analyst should report the raw metric that matches the request's specificity.

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Churn trend

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Churn rate percentage

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Churn probability

Question 7

Which TWO color choices are appropriate for a categorical data visualization? (Select two.)

Accepted Answer

Distinct hues. Option A (Distinct hues) is correct because categorical data represents discrete, unordered groups, so each category needs a visually distinct color that is easily told apart from the others. Option E (Colorblind-friendly palette) is correct because an appropriate categorical visualization must remain distinguishable for viewers with color vision deficiencies, so palettes chosen to be perceptually separable across common types of color blindness are the right choice. Sequential color schemes (B) are designed to encode ordered, continuous magnitude rather than unordered categories, so they do not fit. Monochrome (C) uses variations of a single hue, which implies ordering and offers too little separation for distinct categories. A rainbow gradient (D) is a continuous, ordered spectrum that can create false impressions of ranking and is not reliably discriminable, so it is not appropriate for categorical data.

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Sequential color scheme

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Monochrome

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Rainbow gradient

Question 8

An analyst creates a dashboard with multiple visualizations. Which feature allows users to change the data displayed across all charts simultaneously?

Accepted Answer

Filter or slicer. A filter or slicer is an interactive control that, when changed, applies the same selection criterion to every visualization on the dashboard, enabling simultaneous cross-chart updates. This is the standard mechanism in BI tools (Tableau, Power BI, Looker) for synchronized data selection.

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Linked chart

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Drill-down

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Data segmentation

Question 9

An analyst needs to identify outliers in a numeric column 'transaction_amount' using the interquartile range (IQR) method. Which TWO steps are part of this process? (Select TWO).

Accepted Answer

Subtract 1.5 times the IQR from Q1 and add 1.5 times the IQR to Q3 to define bounds. The IQR method requires first calculating the first quartile (Q1) and third quartile (Q3) of the numeric column, since the IQR is defined as Q3 minus Q1 — this makes option C correct. Once Q1, Q3, and the IQR are known, the outlier bounds are established by subtracting 1.5 × IQR from Q1 (lower fence) and adding 1.5 × IQR to Q3 (upper fence), so option A is correct. Values falling outside these fences are flagged as outliers. Option B is not required because the median is not used in the IQR fence calculation. Option D describes trimming extremes by percentile, which is a different technique and not part of the IQR method. Option E describes a z-score approach using mean and standard deviation, which is an alternative outlier-detection method, not the IQR method.

Answer

Calculate the median of the column

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Sort the data and remove the top and bottom 5%

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Compute the mean and standard deviation of the column

Question 10

Refer to the exhibit. A data analyst runs this query to identify high-value customers. However, the result does not include customers with exactly 5 orders. Which data concept does the HAVING clause illustrate?

Accepted Answer

Data aggregation with filtering on aggregated values. The HAVING clause filters groups after aggregation, so it operates on aggregated values like COUNT(*), SUM(), or AVG(). In the exhibit, the query likely uses HAVING COUNT(order_id) > 5, which excludes customers with exactly 5 orders because the condition is strictly greater than 5. This illustrates aggregation with post-aggregation filtering, distinct from WHERE, which filters rows before grouping.

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Data sorting with ORDER BY

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Data joining with INNER JOIN

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Data filtering on row-level conditions

Question 11

A marketing manager wants to visualize the conversion rate at each stage of a sales funnel, from leads to closed deals. The data shows the number of prospects at each stage. Which chart type is most appropriate for this pipeline analysis?

Accepted Answer

Funnel chart. A funnel chart is specifically designed to show the progressive reduction of data as it moves through stages, making it ideal for visualizing conversion rates in a sales pipeline. It displays the number of prospects at each stage, with the width of each section representing the quantity, clearly illustrating drop-offs.

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Treemap

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Waterfall chart

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Stacked bar chart

Question 12

An e-commerce company wants to provide real-time personalized product recommendations based on customer browsing behavior. Currently, they have a traditional data warehouse that processes batch updates every night. The marketing team complains that recommendations are outdated within hours because customers see yesterday's data. The data engineer needs to modify the architecture to support near-real-time analytics. The budget is limited, and the existing warehouse infrastructure must be reused as much as possible. Which architectural change would best meet the requirement?

Accepted Answer

Implement a streaming data pipeline (e.g., Apache Kafka) that feeds a real-time recommendation engine.. Implementing a streaming data pipeline like Apache Kafka enables the ingestion and processing of customer browsing events in near real-time, feeding a dedicated recommendation engine that can update recommendations within seconds or minutes. This approach reuses the existing data warehouse for historical analytics and batch reporting while adding a lightweight streaming layer for low-latency recommendations, aligning with the limited budget and reuse requirement.

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Replace the warehouse with an in-memory database for real-time processing.

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Add more nodes to the warehouse cluster to speed up batch processing.

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Increase the frequency of batch load from nightly to every hour.

Question 13

A data analyst is tasked with gathering data from a legacy system that only exports CSV files. The files contain headers but no data types. Which tool would best facilitate initial data exploration?

Accepted Answer

Python pandas. Python pandas is the best tool for initial data exploration of CSV files because its read_csv() function automatically infers data types, handles headers, and provides immediate exploratory methods like .info(), .describe(), and .head(). It requires no schema definition upfront, making it ideal for legacy exports with unknown types.

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Hadoop

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Tableau

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SQL database

Question 14

A data analyst runs the query: SELECT AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 60000. What is the purpose of the HAVING clause?

Accepted Answer

It filters departments where the average salary exceeds $60,000.. HAVING filters groups after aggregation, so HAVING AVG(salary) > 60000 removes entire department groups whose computed average salary does not exceed $60,000. It operates on the aggregated result, not on individual rows, which is why it can reference aggregate functions like AVG.

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It orders departments by average salary descending.

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It returns only the department with the maximum average salary.

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It filters individual employee rows with salary > 60000 before grouping.

Question 15

Which THREE data quality dimensions are commonly assessed in a data profiling task?

Accepted Answer

Consistency. Consistency is a core data quality dimension assessed in data profiling because it evaluates whether data values are free from contradiction and adhere to the same representation rules across records. In profiling tools like Informatica or Talend, consistency checks identify violations such as 'NY' vs 'New York' in a state column, ensuring semantic uniformity.

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Scalability

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Availability