DA0-002 Practice Test 17 — 15 Questions

Question 1

Which TWO of the following are best practices when creating a data visualization for a presentation to stakeholders?

Accepted Answer

Including interactive filters when possible to allow stakeholders to explore data. Interactive filters (e.g., slicers, parameter controls) empower stakeholders to drill down into specific data subsets, ask ad-hoc questions, and uncover insights without overwhelming the initial view. This aligns with the principle of user-driven exploration, which is a best practice for stakeholder presentations where different audience members may have varying interests. Including interactivity also reduces the need for multiple static charts, keeping the presentation concise while still offering depth.

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Placing the legend at the bottom of the chart

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Using 3D effects to make the chart visually appealing

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Using only one type of chart throughout the presentation for consistency

Question 2

Which THREE are challenges in acquiring data from external sources? (Select three.)

Accepted Answer

Licensing restrictions. Licensing restrictions (C) are a genuine external-data acquisition challenge because third-party providers impose contractual terms, usage caps, and redistribution limits that can legally block or constrain how the data is ingested and reused. Rate limiting (D) is correct because external APIs and feeds typically enforce request quotas (e.g., HTTP 429 responses, tokens per minute), forcing throttling, retries, or batching that complicate acquisition. Data format inconsistency (E) is correct because external sources deliver data in heterogeneous schemas and encodings (JSON, XML, CSV, proprietary formats, differing date/number conventions), requiring parsing and normalization before use. Data redundancy (A) is an internal data-quality issue about duplicate records rather than an external acquisition obstacle, and unauthorized access (B) is a security concern affecting data protection, not a challenge inherent to acquiring data from outside sources.

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Data redundancy

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Unauthorized access

Question 3

A data analyst needs to show the relationship between advertising spend (in dollars) and monthly sales revenue (in dollars) for the past 12 months. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart to show the relationship between two numerical variables, such as advertising spend and sales revenue. It plots each pair of values as a point, allowing the analyst to visually assess correlation, trends, and outliers. Line charts are better for time series, bar charts for categorical comparisons, and pie charts for proportions.

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Line chart

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Bar chart

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Pie chart

Question 4

A data analyst needs to create a new column 'full_name' by concatenating 'first_name' and 'last_name' with a space. Which SQL function should be used in the SELECT clause?

Accepted Answer

CONCAT(first_name, ' ', last_name). CONCAT(first_name, ' ', last_name) is the standard SQL function for joining strings, and it correctly inserts a literal space between the two columns. CONCAT accepts multiple arguments and returns a single concatenated string, which can be aliased as full_name in the SELECT clause.

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COMBINE(first_name, last_name)

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JOIN(first_name, last_name)

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first_name + ' ' + last_name

Question 5

A data quality assessment reveals that a column named 'email' contains values like 'user@example' (missing domain extension). Which data profiling technique would best identify such pattern violations?

Accepted Answer

Pattern analysis. Pattern analysis examines the format and structure of values against an expected pattern (e.g., a regex for valid emails), making it the right technique to detect values like 'user@example' that violate the expected email format. It surfaces format inconsistencies, not just missing or duplicate values.

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Cardinality analysis

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Referential integrity check

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Data type verification

Question 6

A data analyst is writing a query to rank products by total sales within each category, showing dense rank and avoiding gaps. Which window function should be used?

Accepted Answer

DENSE_RANK(). DENSE_RANK() assigns ranks without gaps when ties occur — if two products tie for rank 1, the next product gets rank 2, not 3. This matches the requirement to 'avoid gaps' while still assigning equal ranks to ties. It is used with OVER (PARTITION BY category ORDER BY total_sales DESC).

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ROW_NUMBER()

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NTILE()

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RANK()

Question 7

A data analyst wants to ensure a sample proportionally represents different regions in a population. Which sampling method should be used?

Accepted Answer

Stratified sampling. Stratified sampling divides the population into distinct subgroups (strata) based on a shared characteristic — here, region — and then draws a proportional random sample from each stratum. This guarantees that each region is represented in the sample in proportion to its size in the population, which is exactly what the analyst requires. Simple random sampling could, by chance, under- or over-represent certain regions.

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Simple random sampling

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Cluster sampling

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Systematic sampling

Question 8

When communicating uncertainty in a report, which of the following is the most appropriate way to convey the reliability of a survey result showing 75% customer satisfaction?

Accepted Answer

"We are 95% confident that the true satisfaction rate is between 72% and 78%.". A confidence interval communicates both the point estimate and the uncertainty around it. Stating '95% confident the true rate is between 72% and 78%' gives the reader the estimate (75%), the precision (the interval width), and the confidence level, which is the most complete and honest way to convey reliability. The other options either omit the uncertainty or state it incompletely.

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"The satisfaction rate might be lower or higher."

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"75% of customers are satisfied."

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"The margin of error is 3%."

Question 9

After a marketing campaign, sales increased by 15%. The analyst wants to understand which customer segment contributed most to the increase. Which type of analysis is this?

Accepted Answer

Diagnostic analysis. Diagnostic analysis is used to understand the root cause of an event or change. In this scenario, the analyst already knows sales increased by 15% and wants to determine which customer segment drove that increase, which is a classic diagnostic question. This type of analysis goes beyond describing what happened to explain why it happened.

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Predictive analysis

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Prescriptive analysis

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Descriptive analysis

Question 10

A data engineer is designing a data warehouse for a multinational corporation. The company has sales data from different regions with varying currencies and date formats. To ensure consistency, which data concept should be applied to standardize the data before loading into the warehouse?

Accepted Answer

Data transformation. Data transformation is the correct concept because it involves converting data from source formats (e.g., different currencies and date formats) into a consistent, standardized format before loading into the data warehouse. This process includes applying conversion rules, such as using ISO 8601 for dates and a single base currency (e.g., USD) with exchange rate tables, ensuring uniformity across all regional data. Without transformation, the warehouse would contain incompatible data types, breaking referential integrity and analytical queries.

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Data cleansing

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Data profiling

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Data masking

Question 11

A logistics company is analyzing truck delivery times. Which variable is discrete?

Accepted Answer

Number of stops. A discrete variable is one that takes on a countable number of distinct values, often integers. The number of stops a truck makes is a count (e.g., 0, 1, 2, 3) and cannot be a fraction, making it a classic discrete variable in data analysis.

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Time taken in hours

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Fuel consumption in liters

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Distance traveled

Question 12

In SQL, which string function would you use to remove leading and trailing spaces from a column named 'city'?

Accepted Answer

TRIM. The SQL TRIM function removes both leading and trailing spaces (or specified characters) from a string. Applied to a column like 'city', TRIM(city) returns the value with all surrounding whitespace stripped, which is exactly what the question asks for.

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RTRIM

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LTRIM

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CLEAN

Question 13

A data analyst notices that a column labeled 'Income' contains values like '$50,000' and '$75,000', but also 'High' and 'Low'. What data concept issue is occurring?

Accepted Answer

Mixing quantitative and qualitative data. The 'Income' column contains both numeric values (e.g., '$50,000', '$75,000') which are quantitative data, and categorical labels ('High', 'Low') which are qualitative data. Mixing these two distinct data types in a single column violates data consistency principles and prevents proper statistical analysis or machine learning processing. This is a classic example of mixing quantitative and qualitative data.

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Mixing discrete and continuous data

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Mixing nominal and ordinal data

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Mixing structured and unstructured data

Question 14

A data analyst creates a dashboard for executives that shows a key metric trending downward. The analyst notices that the metric is highly volatile day-to-day. Which visualization type is most appropriate to show the underlying trend?

Accepted Answer

7-day moving average line chart.. A 7-day moving average line chart smooths out day-to-day volatility by averaging each point with the preceding six days, revealing the underlying directional trend that executives need to see. Because the metric is highly volatile, plotting raw daily values would obscure the trend in noise; the moving average makes the downward trajectory visually obvious while preserving the time-series nature of the data.

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Pie chart showing proportion of days.

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Scatter plot with regression line.

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Daily bar chart.

Question 15

A data quality report shows that 95% of records have all required fields completed, but 20% of the completed fields contain values that are outside valid ranges. Which data quality dimension is most affected?

Accepted Answer

Accuracy. Accuracy measures how well data reflects real-world values or a defined standard. Here, 20% of completed fields contain values outside valid ranges, meaning the data is present but incorrect, directly degrading accuracy. Completeness (95% filled) is high, but the core issue is that the values themselves are wrong, not missing or late.

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Consistency

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Timeliness

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Completeness