DA0-002 Practice Exam 55 — 20 Questions

Question 1

You are using pandas in Python to clean a dataset. You notice several rows with missing values in the 'age' column. Which method would you use to remove those rows?

Accepted Answer

df.dropna(). df.dropna() removes rows with any missing values by default. df.fillna() fills missing values, df.isna() returns a boolean mask, df.drop_duplicates() removes duplicate rows.

Answer

df.drop_duplicates()

Answer

df.fillna(0)

Answer

df.isna()

Question 2

A data analyst wants to find customers whose last name starts with 'Mc' and have made purchases in 2023. The purchase table has a purchase_date column. Which SQL query accomplishes this?

Accepted Answer

SELECT * FROM customers c JOIN purchases p ON c.id = p.customer_id WHERE last_name LIKE 'Mc%' AND p.purchase_date BETWEEN '2023-01-01' AND '2023-12-31';. The correct query (option D) joins the customers and purchases tables on customer ID to link customers with their purchases. It uses `LIKE 'Mc%'` to match last names starting with 'Mc' (the wildcard '%' matches any sequence of characters) and filters purchase dates within the year 2023 using `BETWEEN '2023-01-01' AND '2023-12-31'`. Options A, B, and C are incorrect: A uses `LIKE 'Mc_'` which matches exactly two characters after 'Mc', not any sequence; B uses `LIKE '%Mc%'` which matches 'Mc' anywhere in the name, not just the beginning; C uses `= 'Mc%'` which treats the wildcard as a literal character; and all three fail to join the purchases table, so they cannot filter by purchase date.

Answer

SELECT * FROM customers WHERE last_name LIKE 'Mc_' AND YEAR(purchase_date) = 2023;

Answer

SELECT * FROM customers WHERE last_name LIKE '%Mc%' AND purchase_date = 2023;

Answer

SELECT * FROM customers WHERE last_name = 'Mc%' AND YEAR(purchase_date) = 2023;

Question 3

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

Answer

Data custodian

Answer

Data steward

Question 4

A business user wants to explore sales data without relying on the data team. Which report type empowers the user to create their own analysis?

Accepted Answer

Self-service report. Self-service reports allow users to query and analyze data independently using BI tools.

Answer

Ad hoc report

Answer

Operational report

Answer

Scheduled report

Question 5

Match each data analysis tool to its primary function.

Accepted Answer

Excel: Spreadsheet analysis and data manipulation. Excel is for spreadsheets, SQL for databases, Python for programming/ML, and Tableau for visualization. Common confusions include assigning predictive modeling to Excel or dashboard creation to SQL.

Answer

Excel: Predictive modeling and statistical analysis

Answer

SQL: Creating interactive dashboards

Question 6

Match each data type to its example.

Accepted Answer

Nominal: Colors of cars (red, blue, green). Nominal data are categories without order; ordinal data have ranked categories; interval data have equal intervals but no true zero; ratio data have equal intervals and a true zero. Common confusions include mistaking nominal for ordinal (e.g., gender) or interval for ratio (e.g., counts).

Answer

Ordinal: Gender (male, female)

Answer

Interval: Number of siblings (0,1,2,3)

Question 7

A data analyst wants to visualize the number of website visitors by traffic source (e.g., organic, paid social, email) and also show the proportion of each source within the total. Which chart type is best?

Accepted Answer

Stacked bar chart. A stacked bar chart is best because it allows the data analyst to display both the absolute number of visitors per traffic source and the proportional contribution of each source to the total across categories (e.g., time periods). Each bar represents the total visitors, and segments within the bar show the breakdown by source, making it easy to compare both individual source counts and their relative shares.

Answer

Histogram

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Pie chart

Answer

Line chart

Question 8

A healthcare organization acquires data from multiple hospitals with different patient record systems. The data includes patient IDs but no common identifier across systems. Which technique should be used to link records?

Accepted Answer

Probabilistic record linkage using name, DOB, and ZIP. (probabilistic linkage) is designed for such situations. Option A (merge without deduplication) creates duplicates and loses connections. Option B (random unique IDs) loses connections because there is no common identifier across systems. Option C (manual matching) is not scalable.

Answer

Merge all records without deduplication

Answer

Generate random unique IDs for each system

Answer

Manually match records for all patients

Question 9

A data analyst is creating a report for a marketing campaign. The campaign data includes customer names, email addresses, and purchase history. Which of the following best describes the 'customer name' data type?

Accepted Answer

Nominal. Customer names are categorical labels that identify individuals without any inherent order or numerical value. This fits the definition of nominal data, which is used for naming or classifying variables. In data analysis, nominal data can be stored as strings and used for grouping or filtering, but arithmetic operations are meaningless.

Answer

Quantitative

Answer

Ordinal

Answer

Discrete

Question 10

A data analyst is profiling a new dataset containing customer information. When assessing data quality, which metric would be most appropriate to determine if the 'email' column contains valid email addresses?

Accepted Answer

Pattern analysis. Pattern analysis (e.g., using regular expressions) can validate whether strings match the expected format of an email address. Row counts, null counts, and cardinality do not validate format.

Answer

Null count

Answer

Cardinality

Answer

Row count

Question 11

After presenting a data analysis to stakeholders, they ask for additional details on a specific trend. The analyst needs to provide a follow-up report. What is the best approach?

Accepted Answer

Provide a detailed written report with charts and explanations.. A detailed written report with charts and explanations allows stakeholders to review at their own pace and provides depth. Option A is wrong because creating a new dashboard for every request is inefficient. Option B is wrong because raw data files lack context and interpretation. Option D is wrong because holding another meeting delays information.

Answer

Create a new dashboard with all the data.

Answer

Send the raw data file to the stakeholders.

Answer

Hold another meeting to verbally explain the trend.

Question 12

An analyst runs an A/B test with 1000 users per group and observes a conversion rate of 5% in the control and 6% in the treatment. The p-value is 0.12. What should the analyst conclude?

Accepted Answer

The difference is not statistically significant at the 0.05 level.. Since p-value > 0.05, we fail to reject the null hypothesis; the observed difference is not statistically significant.

Answer

The sample size is too small to detect an effect.

Answer

The treatment significantly outperforms control.

Answer

There is a 12% chance the treatment is better.

Question 13

A data analyst needs to identify duplicate customer records based on email and phone number. Which SQL techniques can be used to find duplicates? (Select TWO).

Accepted Answer

Use a CTE to assign ROW_NUMBER() and then select rows where rn > 1. GROUP BY with COUNT and HAVING COUNT > 1 filters groups with duplicates. ROW_NUMBER() with PARTITION BY can assign row numbers to identify duplicates.

Answer

SELECT email, phone FROM customers ORDER BY email, phone

Answer

SELECT DISTINCT email, phone FROM customers

Answer

SELECT email, phone, ROW_NUMBER() OVER (PARTITION BY email, phone ORDER BY customer_id) AS rn FROM customers WHERE rn > 1

Question 14

A data analyst needs to calculate the running total of sales for each product over time. Which window function clause is essential for this calculation?

Accepted Answer

PARTITION BY product_id ORDER BY sale_date. For a running total of sales for each product over time, you must group the calculation by product (PARTITION BY product_id) and order by sale_date (ORDER BY sale_date). The default window frame in SQL (RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) suffices for running totals. Option B provides both PARTITION BY product_id and ORDER BY sale_date, making it essential.

Answer

ORDER BY sale_date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW

Answer

PARTITION BY product_id

Answer

ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

Question 15

A sales dashboard shows a map with many overlapping markers in the same city, making it hard to read. What is the best improvement?

Accepted Answer

Aggregate the data by region and use a choropleth map. Aggregating sales data by region and using a choropleth map eliminates visual clutter from overlapping markers by shading entire geographic areas based on a metric (e.g., total sales). This approach leverages spatial aggregation to provide a clear, high-level view of regional performance, which is the best practice when individual point markers become unreadable due to density.

Answer

Add tooltips to show details on hover

Answer

Use a bubble chart instead of a map

Answer

Use different marker colors for each store

Question 16

A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which method of handling missing values is least likely to introduce bias if the missingness is completely at random?

Accepted Answer

Listwise deletion. Listwise deletion (removing rows with missing values) is simple and unbiased if data is MCAR, but it reduces sample size. However, it is least likely to introduce bias among the options when MCAR holds.

Answer

Mean imputation

Answer

Mode imputation

Answer

Forward-fill

Question 17

Which chart type is best for showing the number of website visitors at each stage of a conversion funnel, from initial visit to purchase?

Accepted Answer

Funnel chart. A funnel chart is specifically designed to visualize the progressive reduction in volume across stages of a linear process, such as a conversion funnel. It clearly shows the number of visitors at each stage (e.g., initial visit, product view, add to cart, purchase) and the drop-off between them, making it the optimal choice for this scenario.

Answer

Stacked bar chart

Answer

Treemap

Answer

Waterfall chart

Question 18

Which THREE are best practices for data profiling during acquisition? (Choose three.)

Accepted Answer

Check for completeness. Checking for completeness (Option B) is a best practice during data acquisition because it ensures that all required fields and records are present before further processing. Incomplete data can lead to incorrect analysis or failed transformations, so profiling for missing values or nulls is a fundamental validation step.

Answer

Immediately normalize data

Answer

Skip validation for trusted sources

Question 19

A data analyst needs to combine customer data from two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount). Only customers who have placed at least one order should be included. Which JOIN type should be used?

Accepted Answer

INNER JOIN. An INNER JOIN returns only rows where there is a match in both tables. Since the requirement is to include only customers who have placed at least one order, the INNER JOIN on CustomerID will filter out any customer without a matching order record, exactly meeting the condition.

Answer

LEFT JOIN

Answer

FULL OUTER JOIN

Answer

RIGHT JOIN

Question 20

A retailer wants to test if a new website layout increases the average time spent on the site. They split traffic: control group (old layout) and treatment group (new layout). Which statistical test is most appropriate to compare the average time spent between the two groups?

Accepted Answer

Two-sample t-test. A t-test is used to compare means of two independent groups.

Answer

ANOVA

Answer

Pearson correlation

Answer

Chi-square test