DA0-002 Practice Test 33 — 15 Questions

Question 1

A data analyst wants to understand the relationship between advertising spend and sales revenue. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following is the best interpretation?

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

Accepted Answer

There is a strong positive linear relationship between advertising spend and sales.. A Pearson correlation coefficient of 0.85 indicates a strong positive linear relationship between the two variables. The value is close to +1, meaning as advertising spend increases, sales revenue tends to increase in a linear fashion. Correlation measures the strength and direction of a linear association, not causation or predictive proportion.

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85% of the variation in sales is explained by advertising spend.

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Increasing advertising spend by $1 will increase sales by $0.85.

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There is a strong negative linear relationship between advertising spend and sales.

Question 2

In a multiple regression model with three predictors, the coefficient for one predictor is 5.2 with a p-value of 0.001. Which of the following is the best interpretation?

Accepted Answer

The predictor has a statistically significant effect on the dependent variable, controlling for other variables.. A coefficient of 5.2 with a p-value of 0.001 means that, holding the other predictors constant, a one-unit increase in this predictor is associated with a 5.2-unit increase in the dependent variable, and the effect is statistically significant at conventional thresholds (p < 0.05). The phrase 'controlling for other variables' is key in multiple regression — the coefficient represents the partial effect of this predictor given the others in the model.

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The predictor explains 5.2% of the variance in the dependent variable.

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A one-unit increase in the predictor decreases the dependent variable by 5.2 units, on average.

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The model is not a good fit because one predictor is significant.

Question 3

A data engineer needs to acquire data from a legacy mainframe system that does not support modern APIs or direct database connectivity. Which approach is most feasible?

Accepted Answer

Use FTP to transfer flat files. FTP (File Transfer Protocol, RFC 959) is a widely supported, low-overhead method for transferring flat files (e.g., CSV, EBCDIC-encoded text) from legacy mainframe systems that lack modern APIs or direct database connectivity. Mainframes like IBM z/OS natively support FTP, allowing the data engineer to schedule periodic file exports without requiring system modernization or complex middleware.

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Re-platform the mainframe to a modern system

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Use a database gateway

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Manual data entry

Question 4

In a multiple regression model, one predictor has a high p-value (0.45). What should the analyst consider doing?

Accepted Answer

Remove the predictor from the model. A p-value of 0.45 is far above the conventional significance threshold (typically 0.05), meaning there is no statistically significant evidence that this predictor is associated with the outcome variable after accounting for the other predictors. Removing non-significant predictors simplifies the model, reduces multicollinearity risk, and improves interpretability without meaningful loss of predictive power. This is the standard first step in backward elimination-style model refinement.

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Transform the predictor

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Keep the predictor regardless

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Increase the sample size

Question 5

A data analyst is extracting data from a web page using web scraping techniques. The data will be used for market research. Which TWO of the following are common challenges associated with web scraping?

Accepted Answer

Legal and ethical restrictions. Web scraping often involves accessing data that may be protected by copyright, terms of service, or privacy regulations such as GDPR or the Computer Fraud and Abuse Act (CFAA). Even if data is publicly accessible, repurposing it for market research without permission can lead to legal liability or ethical violations, making this a fundamental challenge.

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Limited API rate limits

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High latency of data transfer

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Inconsistent data formatting

Question 6

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 7

In a customer database, each row represents a customer with columns: CustomerID, Name, Address, Phone. What does the column "Name" represent?

Accepted Answer

Attribute. In the context of a relational database, a column represents an attribute of an entity. The 'Name' column stores a specific characteristic (the customer's name) for each row, making it an attribute. This aligns with the data modeling concept where attributes define the properties of an entity.

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Instance

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Entity

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Record

Question 8

Which THREE are best practices for designing a dashboard for executive consumption?

Accepted Answer

Ensure the dashboard is responsive for mobile devices. Option B is correct because executives frequently consume dashboards on tablets and phones, so a responsive layout ensures KPIs remain legible and usable across screen sizes. Option D is correct because interactive filters for time periods and regions let executives drill into the specific slice of data relevant to their decision without needing a new report. Option E is correct because placing the most critical KPIs at the top follows the principle of prioritized information hierarchy, ensuring the most important metrics are seen first. Option A is not ideal because detailed raw data tables overwhelm executive viewers and obscure high-level trends. Option C is not ideal because fragmenting every metric into its own chart creates visual clutter and prevents at-a-glance comparison of related KPIs.

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Include detailed raw data tables for transparency

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Use a separate chart for each metric to avoid clutter

Question 9

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

Accepted Answer

Mode. The mode is the measure of central tendency that identifies the most frequently occurring value in a dataset. Unlike the mean or median, the mode directly counts the frequency of each distinct value and returns the value with the highest count, making it the correct choice for this specific requirement.

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Standard deviation

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Median

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Mean

Question 10

A sales manager wants to see the conversion rates at each stage of the sales pipeline, from initial contact to closed deal. Which chart type is most appropriate?

Accepted Answer

Funnel chart. A funnel chart is purpose-built to show sequential stages of a process and the progressive reduction in count or value at each stage, making it ideal for visualizing sales pipeline conversion from initial contact to closed deal. The tapering shape visually communicates drop-off at each stage, which is exactly what the sales manager needs.

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Waterfall chart

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Treemap

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Bar chart

Question 11

Which TWO of the following are benefits of database normalization to 3NF? (Select 2)

Accepted Answer

Reduces data redundancy. Normalization to 3NF eliminates transitive dependencies, which directly reduces data redundancy by ensuring each non-key attribute depends only on the primary key. This reduction in redundancy also increases data integrity because updates, inserts, and deletes are less likely to create inconsistencies or anomalies. In a relational database, 3NF achieves this without sacrificing the ability to reconstruct the original data via joins.

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Improves query performance for all queries

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Simplifies complex joins

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Eliminates all data anomalies

Question 12

A data analyst is creating a dashboard for a retail company. The dashboard should provide insights into sales performance across multiple dimensions. Which TWO chart types are best suited for showing the contribution of each product category to total sales?

Accepted Answer

Pie chart. A pie chart is ideal for showing the contribution of each product category to total sales because it visually represents parts of a whole, making it easy to compare proportions at a glance. The stacked bar chart also effectively shows category contributions within a total, allowing for both absolute and relative comparisons across different time periods or segments. Both chart types directly address the need to visualize proportional breakdowns of a single aggregate metric.

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Scatter plot

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Histogram

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Line chart

Question 13

A data analyst creates a dashboard showing average order value by region. The chart indicates that one region has an unusually high average. Investigation reveals that the region has very few orders, but one large purchase inflates the average. Which data transformation should the analyst apply to improve the visualization?

Accepted Answer

Use median instead of mean for aggregation. The mean is highly sensitive to extreme values, so a single large purchase in a region with few orders skews the average upward and misrepresents the typical order value. The median is a robust measure of central tendency that resists outlier influence, so switching the aggregation from mean to median gives a truer picture of typical order value per region.

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Apply logarithmic scaling to the y-axis

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Change the chart type to a pie chart

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Remove all outliers from the dataset

Question 14

A data analyst is working with a dataset that includes customer names, email addresses, and purchase history. The analyst wants to ensure that each customer is uniquely identified. Which TWO database concepts should be used to enforce uniqueness and link related data?

Accepted Answer

Foreign key. A primary key uniquely identifies each row in a table, ensuring no duplicate customer records. A foreign key links related data across tables by referencing the primary key of another table, enforcing referential integrity. Together, they guarantee uniqueness and enable relational joins between customer and purchase history tables.

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Normalization

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View

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Index

Question 15

A BI analyst is creating a report in Power BI and needs to calculate the total sales for the current year compared to the previous year. Which DAX function should be used to calculate the previous year's sales?

Accepted Answer

SAMEPERIODLASTYEAR. SAMEPERIODLASTYEAR returns a set of dates in the previous year for the same period, which is ideal for year-over-year comparisons.

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PREVIOUSYEAR

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TOTALYTD

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DATEADD