DA0-002 Practice Test 38 — 15 Questions

Question 1

A marketing team conducted a customer satisfaction survey for five different departments (Sales, Support, Billing, Shipping, Returns). The survey asked customers to rate their satisfaction on a scale of 1 (Very Dissatisfied) to 5 (Very Satisfied). The data is ordinal and the team wants to visualize the distribution of responses for each department to quickly see which department has the most 'Very Satisfied' customers and which has the most 'Very Dissatisfied'. They also want to compare the spread of responses across departments. Which chart type should they use?

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

Accepted Answer

Stacked bar chart with departments on x-axis and counts of each rating stacked. The stacked bar chart, is correct because it displays the distribution of ordinal satisfaction ratings for each department, allowing easy comparison of counts for each rating category. With departments on the x-axis and stacked segments for ratings 1-5, the team can quickly identify which department has the most 'Very Satisfied' (rating 5) and 'Very Dissatisfied' (rating 1). The stacked bars also reveal the spread of responses across departments. Option C (box plot) is less suitable because it assumes continuous interval data and does not show exact counts of each ordinal category. Option B (line chart) is for trends over a continuous axis, not categorical comparisons of distributions. Option D (scatter plot) is for relationships between two continuous variables, not for comparing response distributions.

Answer

Line chart with departments on x-axis and average rating on y-axis

Answer

Box plot for each department

Answer

Scatter plot with department as category and satisfaction score as value

Question 2

A sales analyst is building a report to analyze quarterly performance. Which THREE are appropriate uses of a formula language or a data transformation tool? (Select three.)

Accepted Answer

Using a data transformation tool to remove duplicate rows from the data source. Data transformation tools are specifically designed for data cleaning and preparation tasks, such as removing duplicate rows from a data source before the data is loaded into the analysis model. This is a standard ETL operation that such tools handle efficiently using their deduplication features, which is not a function of a formula language.

Answer

Using an iterative summation function to sum a column without using iteration

Answer

Using a lookup function to create a calculated column from a related table

Question 3

A database has a table that violates 2NF because it contains a composite primary key and some attributes depend only on part of that key. Which normal form would be violated next if the table is not addressed?

Accepted Answer

3NF. The table already violates 2NF. If not addressed, it will never satisfy 2NF, but the question asks which normal form would be violated next if the table is not addressed. Since 2NF is already violated, the next normal form in sequence that will be violated is 3NF, because the table will fail to meet 3NF requirements (e.g., transitive dependencies) while still being stuck at a 2NF violation. Therefore, 3NF is the next normal form that would be violated.

Answer

2NF

Answer

BCNF

Answer

1NF

Question 4

A dataset contains a column 'birthdate' in 'YYYY-MM-DD' format. The analyst needs to calculate the average age of customers as of today. Which combination of functions is most appropriate?

Accepted Answer

DATEDIFF(year, birthdate, GETDATE()). DATEDIFF(year, birthdate, GETDATE()) returns the number of year boundaries crossed between the two dates, which is the standard SQL method for calculating age. The other options subtract year components, which gives only an estimate that ignores the month and day, leading to inaccuracies.

Answer

AVG(YEAR(GETDATE()) - YEAR(birthdate))

Answer

YEAR(GETDATE()) - YEAR(birthdate)

Answer

EXTRACT(YEAR FROM GETDATE()) - EXTRACT(YEAR FROM birthdate)

Question 5

Drag and drop the steps to clean a dataset with missing values in the correct order.

Accepted Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes. Cleaning starts with identification, then analysis of missingness, strategy selection, application, and documentation.

Answer

Step 1: Analyze missingness pattern, Step 2: Identify missing values, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes

Answer

Step 1: Identify missing values, Step 2: Select imputation/removal strategy, Step 3: Analyze missingness pattern, Step 4: Apply the strategy, Step 5: Document changes

Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Apply the strategy, Step 4: Document changes, Step 5: Select imputation/removal strategy

Question 6

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.

Answer

Instance

Answer

Entity

Answer

Record

Question 7

A data analyst is troubleshooting a map visualization that shows null values for some regions. Which TWO actions should the analyst take to resolve the issue?

Accepted Answer

Check that latitude and longitude fields are properly geocoded.. Improper geocoding of latitude and longitude fields can result in null values on a map. Option B is correct because missing region names in the data source cause nulls; verifying and correcting the data source resolves the issue. Option C is incorrect because filtering out nulls hides the problem without fixing the underlying data. Option D is incorrect because changing the mark type does not address missing data. Option E is incorrect because removing color encoding only alters the visual appearance, not the data integrity.

Answer

Add a filter to exclude null values.

Answer

Change the mark type from Map to Pie.

Answer

Remove the color encoding from profit.

Question 8

An analyst presents a report to stakeholders who are not data-savvy. The report includes a box plot showing the distribution of customer satisfaction scores. One stakeholder asks, 'What do the whiskers mean?' Which communication strategy should the analyst use?

Accepted Answer

Explain that the whiskers show the range of typical scores, like the spread of data.. It uses plain language ('range of typical scores') to explain whiskers to a non-technical audience, aligning with the DA0-001 domain of communicating data insights effectively. The whiskers in a box plot typically extend to the minimum and maximum values within 1.5 times the interquartile range (IQR), representing the spread of data without outliers, which is accurately described as 'typical scores' for stakeholders who are not data-savvy.

Answer

Provide a handout with definitions of box plot elements.

Answer

Replace the box plot with a bar chart of average scores.

Answer

State that the whiskers represent the minimum and maximum values excluding outliers.

Question 9

Drag and drop the steps to perform a root cause analysis on data quality issues in the correct order.

Accepted Answer

Define the problem, Gather data, Identify potential causes, Determine root cause, Implement corrective actions. Root cause analysis involves defining the problem, gathering data, identifying causes, finding root cause, and acting.

Answer

Gather data, Define the problem, Identify potential causes, Determine root cause, Implement corrective actions

Answer

Define the problem, Identify potential causes, Determine root cause, Gather data, Implement corrective actions

Answer

Implement corrective actions, Define the problem, Gather data, Identify potential causes, Determine root cause

Question 10

A data engineer is designing a data pipeline for a retail company. The source system is an OLTP database that records sales transactions. The target is a data warehouse used for reporting. The engineer is evaluating whether to use ETL or ELT. Which three factors would favor using ELT over ETL? (Select THREE)

Accepted Answer

The business analysts need access to raw data for ad-hoc exploration. ELT loads raw data into the warehouse first, allowing business analysts to perform ad-hoc exploration directly on the source data without pre-transformation. This flexibility is a key advantage of ELT over ETL, where transformations are applied before loading.

Answer

The transformation logic requires proprietary functions not available in the warehouse

Answer

Data must be cleansed and validated before loading into the warehouse

Question 11

A data analyst uses the elbow method to determine the number of clusters for k-means. The plot shows a sharp bend at k=3 and a small bend at k=5. What is the recommended number of clusters?

Accepted Answer

3. The elbow method suggests choosing k where the decrease in inertia becomes marginal; the sharp bend at 3 indicates the optimal k.

Answer

5

Answer

The method is inconclusive.

Answer

2

Question 12

A data scientist is analyzing a dataset with 100 variables and 5,000 records. The dataset has several missing values and a few extreme outliers. The goal is to build a regression model to predict a continuous target. Which combination of preprocessing steps is most likely to improve model performance?

Accepted Answer

Impute missing values with median, apply robust scaling, and then log transform skewed variables. Imputing missing values with the median is robust to outliers, robust scaling handles extreme values by using median and IQR, and log transformation reduces skewness in predictors. This combination preserves data integrity and stabilizes variance, which is critical for regression models on a dataset with 100 variables and 5,000 records.

Answer

Impute missing values with mean, then use PCA for dimensionality reduction

Answer

Drop all rows with missing values, then apply min-max scaling

Answer

Remove outliers using Z-score, then apply standard scaling

Question 13

In a dataset with variables on different scales (e.g., age in years and income in dollars), which preprocessing step is necessary before applying k-means clustering?

Accepted Answer

Normalization (scaling). K-means clustering relies on Euclidean distance to measure similarity between data points. When variables like age (in years) and income (in dollars) are on different scales, the variable with larger numeric values (income) will dominate the distance calculation, skewing the clustering results. Normalization (scaling), such as min-max scaling or z-score standardization, rescales all features to a comparable range (e.g., [0,1] or mean=0, variance=1), ensuring each feature contributes equally to the distance computation.

Answer

Feature selection

Answer

Dimensionality reduction

Answer

One-hot encoding

Question 14

After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?

Accepted Answer

Increase minimum samples per leaf. The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.

Answer

Increase number of estimators

Answer

Decrease minimum samples per split

Answer

Increase maximum depth

Question 15

A marketing analyst needs to combine customer data from a CRM database with social media engagement data from a third-party API. Which data acquisition method is most appropriate?

Accepted Answer

API integration. API integration is the most appropriate method because it allows the analyst to programmatically retrieve structured social media engagement data directly from the third-party service's RESTful or GraphQL API endpoints. This approach ensures real-time or near-real-time data synchronization, supports authentication (e.g., OAuth 2.0), and returns data in standardized formats like JSON or XML, which can be directly ingested into the CRM system without manual intervention.

Answer

Web scraping

Answer

Manual data entry

Answer

Batch file upload