DA0-002 Practice Exam 51 — 20 Questions

Question 1

A healthcare analyst is creating a report on patient outcomes. To comply with privacy regulations, which action should be taken before publishing?

Accepted Answer

Anonymize personally identifiable information. Anonymizing PII protects patient identities and ensures compliance with privacy laws like HIPAA.

Answer

Include patient names for context

Answer

Provide raw data to all stakeholders

Answer

Aggregate data without anonymization

Question 2

A data analyst is preparing a presentation to share findings with non-technical stakeholders. Which TWO practices should the analyst follow to effectively communicate data insights? (Choose two.)

Accepted Answer

Tailor the message to the audience. Option A, 'Tailor the message to the audience,' is correct because effective data communication requires framing findings in terms and context that non-technical stakeholders can readily understand, such as using plain language, relevant business metrics, and analogies instead of assuming technical background. Option E, 'Focus on actionable insights,' is correct because the primary purpose of presenting to stakeholders is to drive decisions, so the analyst should emphasize what the data means and what actions should follow rather than merely describing the data. Option B, 'Provide raw data tables,' is not appropriate because dumping unprocessed tables overwhelms non-technical audiences and obscures the key takeaways. Option C, 'Use technical jargon to demonstrate expertise,' is wrong because jargon creates barriers and alienates the audience rather than clarifying insights. Option D, 'Include all data anomalies and outliers,' is not suitable because exhaustive detail distracts from the main message; anomalies should only be mentioned if they materially affect the conclusions.

Answer

Provide raw data tables

Answer

Use technical jargon to demonstrate expertise

Answer

Include all data anomalies and outliers

Question 3

A data analyst wants to show the sales trend for a product over the past 12 months. Which chart type is most appropriate?

Accepted Answer

Line chart. A line chart is the most appropriate choice because it explicitly shows data points over a continuous time interval, making it ideal for visualizing the sales trend over 12 months. The x-axis represents time (months), and the y-axis represents sales values, allowing the audience to easily see upward, downward, or cyclical patterns. This aligns with the core principle of time-series visualization, where line charts excel at highlighting trends and changes over sequential periods.

Answer

Bar chart

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Pie chart

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Histogram

Question 4

An analyst calculates a Pearson correlation coefficient of -0.8 between advertising spend and customer churn rate. Which interpretation is correct?

Accepted Answer

Increasing advertising spend is associated with decreasing churn rate.. A Pearson correlation of -0.8 indicates a strong negative linear relationship, meaning that as advertising spend increases, customer churn rate tends to decrease. The correct interpretation is directional association, not causation — the coefficient describes the strength and direction of the linear relationship between the two variables.

Answer

There is a weak positive relationship.

Answer

Advertising spend causes churn to decrease.

Answer

64% of the variance in churn is explained by spend.

Question 5

An executive dashboard needs to display key performance indicators (KPIs) such as sales growth and customer satisfaction. Which design principle is most important?

Accepted Answer

Consistent color encoding for similar metrics. Consistent color encoding for similar metrics lets viewers instantly associate a color with a metric category (for example, green for growth, red for decline) across the dashboard, reducing cognitive load and misinterpretation. This is a core data visualization principle for executive dashboards where quick, accurate comprehension is critical. It supports preattentive processing, allowing users to spot trends without reading every label.

Answer

Use gauges for each KPI

Answer

Include all raw data in the dashboard

Answer

Animate charts to draw attention

Question 6

An organization needs to store raw data from IoT sensors in its native format for future analysis. Which storage solution is best suited for this purpose?

Accepted Answer

Data lake. A data lake is designed to store raw data in its native format, including unstructured and semi-structured data from IoT sensors, without requiring a predefined schema. This allows the organization to preserve the original data for future analysis, unlike traditional databases that enforce structure upon ingestion.

Answer

Relational database

Answer

Data mart

Answer

Data warehouse

Question 7

A dataset has missing values in the 'age' column. The distribution of age is approximately normal with few outliers. Which imputation method is most appropriate?

Accepted Answer

Mean imputation. Mean imputation is appropriate when the data is approximately normally distributed and has few outliers, because the mean is a representative measure of central tendency for symmetric distributions. It preserves the overall mean of the variable and is simple to implement. Forward-fill and mode imputation are better for time-series or categorical data, respectively, and deleting rows can introduce bias and reduce sample size.

Answer

Forward-fill

Answer

Delete all rows with missing data

Answer

Mode imputation

Question 8

A data analyst is cleaning a dataset and finds that 5% of values in the 'income' column are missing. The analyst decides to impute missing values using the mean of the non-missing values. Which potential issue should the analyst be most concerned about?

Accepted Answer

The imputation may reduce the variance and distort the distribution.. Mean imputation replaces missing values with the average of observed values, which pulls imputed values toward the center of the distribution. This artificially reduces the variance and can distort the shape of the distribution (e.g., making it more peaked), biasing downstream statistics and models. The analyst should be most concerned about this distortion rather than the missing rate itself.

Answer

The imputation is not valid because the missing rate is too low.

Answer

The imputation will increase the standard deviation of the variable.

Answer

The imputation will create outliers.

Question 9

A dataset contains a variable 'Income' with many missing values. The analyst decides to impute missing values with the median income of the non-missing values. Which type of imputation is this?

Accepted Answer

Median imputation. Imputing missing values with the median of the non-missing values is exactly median imputation. It is a common method for handling missing data, especially when the distribution is skewed or contains outliers.

Answer

Interpolation

Answer

Deletion

Answer

Forward-fill imputation

Question 10

In Tableau, you want to create a view showing the total sales per region, but also want to allow users to filter by year without losing the ability to see all regions. What feature should you use to compute the total sales that ignores the year filter?

Accepted Answer

LOD expression: { FIXED [Region] : SUM([Sales]) }. A Level of Detail (LOD) expression with FIXED can compute a value at the region level, ignoring other dimensions like year.

Answer

Table calculation running total

Answer

Parameter action

Answer

Calculated field using SUM([Sales])

Question 11

A data analyst wants to generate a report showing employee names and their department names, but some employees are not assigned to any department. The analyst wants to include all employees. Which JOIN type should be used?

Accepted Answer

LEFT JOIN. LEFT JOIN includes all rows from the left table (employees) even if no match in departments.

Answer

INNER JOIN

Answer

CROSS JOIN

Answer

RIGHT JOIN

Question 12

A data analyst wants to concatenate first_name and last_name columns with a space in between. Which string function combination should be used in SQL?

Accepted Answer

CONCAT(first_name, ' ', last_name). CONCAT(first_name, ' ', last_name) is correct because CONCAT accepts multiple arguments and joins them in order; inserting a literal space string ' ' between the two column arguments produces the desired 'first last' format. This is the standard, portable SQL approach for joining columns with a delimiter.

Answer

first_name + ' ' + last_name

Answer

SUBSTRING(first_name, 1, 1) + '.' + last_name

Answer

CONCAT(first_name, last_name)

Question 13

In a Power BI report, a user wants to create a measure that calculates total sales for the current year up to today. Which DAX function should they use?

Accepted Answer

TOTALYTD. TOTALYTD is a time intelligence function that calculates year-to-date values.

Answer

CALCULATE

Answer

SUMX

Answer

SAMEPERIODLASTYEAR

Question 14

Refer to the exhibit. What data quality issue is indicated?

Accepted Answer

Non-standardized data entry. The exhibit shows the same logical value entered in multiple inconsistent formats (e.g., 'NY', 'New York', 'new york', 'N.Y.'), which is the hallmark of non-standardized data entry. The data is not duplicated (different spellings), not an outlier (no extreme numeric value), and not a referential inconsistency (no conflicting values across tables) — it is a formatting/standardization problem at the point of capture.

Answer

Data inconsistency

Answer

Outlier

Answer

Data duplication

Question 15

A data governance team is implementing a program to ensure consistent definitions and quality of customer data across the organization. They assign a senior manager to be accountable for the data asset. Which role does this manager fulfill?

Accepted Answer

Data owner. The data owner is the senior manager accountable for a specific data asset, including its quality, definition, and compliance. In the DA0-001 context, the data owner has ultimate responsibility for the data, not just day-to-day management. This role ensures consistent definitions and quality across the organization, aligning with the governance team's objectives.

Answer

Data analyst

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Data custodian

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Data steward

Question 16

A data analyst is designing a data model for a sales data warehouse. The model should optimize query performance for aggregations by minimizing joins and duplicating data where necessary. Which schema design should the analyst use?

Accepted Answer

Star schema. A star schema is the correct choice because it organizes data into a central fact table surrounded by denormalized dimension tables, which minimizes the number of joins required for aggregation queries. By duplicating dimension attributes rather than normalizing them, the star schema trades storage for query speed, making it ideal for data warehouse workloads that emphasize analytical performance. This design directly supports the requirement to optimize aggregations while reducing join complexity.

Answer

Entity-relationship model

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Snowflake schema

Answer

3NF normalized model

Question 17

Which of the following data types best describes a JSON file containing customer orders with varying fields per record?

Accepted Answer

Semi-structured data. JSON (JavaScript Object Notation) is a text-based format that uses key-value pairs and nested objects/arrays, providing a flexible schema where each record can have different fields. This self-describing structure—where data carries its own metadata via keys—is the hallmark of semi-structured data. Unlike structured data, it doesn't require a rigid, predefined schema, but unlike unstructured data, it still has an organized, machine-readable format. Therefore, a JSON file with varying fields per record is best classified as semi-structured data.

Answer

Unstructured data

Answer

Structured data

Answer

Relational data

Question 18

Which of the following is an example of semi-structured data?

Accepted Answer

A JSON file. Semi-structured data has tags or markers to separate data elements, like JSON or XML.

Answer

A CSV file without header

Answer

An image file

Answer

A table in a relational database

Question 19

A retail company operates 50 stores across the country. The data analyst has been asked to create a dashboard to visualize monthly sales trends over the past two years and compare the performance of the top 5 stores. The dataset includes store name, date, and daily sales amount. Initial exploration reveals that some stores have missing sales data for certain months due to system outages, and there are occasional extreme values caused by promotional events (e.g., Black Friday sales are 10x normal). The analyst needs to choose an appropriate visualization approach that accurately represents the trends and comparisons while handling these data quality issues. What should the analyst do to best meet the requirements?

Accepted Answer

Use a line chart for the overall monthly sales trend (aggregated across all stores) and a separate bar chart for the top 5 stores by total sales, with tooltips explaining outlier values.. The analyst should use a line chart for the overall monthly sales trend to show the aggregated pattern over two years, and a separate bar chart for the top 5 stores to compare their performance. Tooltips can explain outlier values (e.g., Black Friday spikes) without distorting the overall trend. This approach handles missing data by aggregating (which can smooth over gaps) and addresses outliers by providing context rather than removing them, which could bias the analysis.

Answer

Use a line chart for each store's monthly sales and exclude outliers from the dataset.

Answer

Use a bar chart showing monthly sales for each month and filter out stores with missing data.

Answer

Use a stacked bar chart with all 50 stores, each bar representing a month.

Question 20

A company uses a data warehouse for reporting. They need to extract data from multiple sources, load it into a staging area, and then transform it before moving to the warehouse. This process is known as:

Accepted Answer

ETL. The process described—extracting data from multiple sources, loading it into a staging area, and then transforming it before moving to the warehouse—is the classic definition of ETL (Extract, Transform, Load). In ETL, transformation occurs after extraction but before loading into the target system, which is exactly what the staging area is used for. This contrasts with ELT, where transformation happens after loading into the warehouse.

Answer

ELT

Answer

Data replication

Answer

Data ingestion