DA0-002 Practice Test 13 — 15 Questions

Question 1

A data analyst is using pandas in Python to merge two DataFrames: sales (columns: sale_id, product_id, amount) and products (columns: product_id, product_name). Which pandas function should they use to combine these DataFrames on the 'product_id' column?

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

Accepted Answer

merge(). The pandas merge function is used to combine DataFrames on common columns. The syntax is pd.merge(sales, products, on='product_id').

Answer

combine()

Answer

join()

Answer

concat()

Question 2

A data analyst is troubleshooting a report that shows unusually high sales for a specific product. Upon investigation, the analyst finds that the product was returned by several customers, but the returns were recorded in a separate system and not reflected in the sales data. Which data integration concept was likely missing?

Accepted Answer

Data reconciliation. The core issue is that the sales data and returns data are inconsistent because they were not cross-verified. Data reconciliation is the process of comparing datasets to ensure they are in agreement and identifying discrepancies, such as returns not being reflected in sales figures. Without reconciliation, the analyst would not detect that the high sales number is inflated by unrecorded returns.

Answer

ETL (Extract, Transform, Load)

Answer

Data profiling

Answer

Data governance

Question 3

To consolidate data from multiple operational databases into a central repository for reporting, a company decides to transform data before loading it into the target system. Which data integration approach is being used?

Accepted Answer

ETL (Extract, Transform, Load). The scenario describes transforming data before loading it into the target system, which is the defining characteristic of ETL (Extract, Transform, Load). In ETL, data is extracted from source systems, transformed in a staging area (e.g., cleaning, aggregating, joining), and then loaded into the central repository. This approach is commonly used when the target system (e.g., a data warehouse) requires pre-processed, high-quality data for reporting.

Answer

Data virtualization

Answer

Change data capture

Answer

ELT (Extract, Load, Transform)

Question 4

An e-commerce company is acquiring product data from multiple supplier APIs. The APIs return JSON with inconsistent field naming conventions. Which data acquisition technique should be applied?

Accepted Answer

Data mapping and transformation. Data mapping and transformation is the correct technique because the JSON responses from different supplier APIs use inconsistent field naming conventions (e.g., 'product_id' vs. 'ProductID'). This technique defines a schema to map source fields to a standardized target format, ensuring data consistency before loading into the company's system. Without transformation, downstream processes like analytics or inventory management would fail due to mismatched field names.

Answer

Data compression

Answer

Data deduplication

Answer

Data aggregation

Question 5

A data analyst frequently receives ad hoc requests for the same type of analysis. Which TWO approaches could reduce the number of ad hoc requests?

Accepted Answer

Create a scheduled report that covers the common analysis. Option B is correct because a scheduled report that covers the common analysis proactively delivers the recurring results the analyst keeps being asked for, eliminating the need for users to submit one-off requests each time. Option E is correct because a self-service BI tool lets business users build and run their own reports against governed data, shifting routine ad hoc demand away from the analyst. Option A is not correct because increasing data freshness to real-time addresses latency, not the volume of repetitive requests. Option C is not correct because adding security controls does not reduce how often users ask for the same analysis. Option D is not correct because ignoring requests is unprofessional and does nothing to eliminate the underlying recurring demand.

Answer

Increase data freshness to real-time

Answer

Add more security to the data

Answer

Ignore the requests until they become urgent

Question 6

A dataset contains transaction amounts with a few extremely high values. The analyst wants to reduce the impact of these outliers on the average. Which measure of central tendency is most robust?

Accepted Answer

Median. Median is not affected by extreme values, while mean is sensitive.

Answer

Mean

Answer

Mode

Answer

Standard deviation

Question 7

A data analyst is performing data profiling on a customer dataset. Which metric would best reveal the number of distinct values in the 'state' column?

Accepted Answer

Cardinality. Cardinality measures the number of distinct values in a column, so it directly reveals how many unique states appear in the 'state' column. This is a standard data profiling metric used to understand column variability and potential categorical encoding needs. Mean, row count, and null count do not provide distinct value counts.

Answer

Mean

Answer

Row count

Answer

Null count

Question 8

During exploratory data analysis, you calculate the IQR for a numeric column and find that several data points fall below Q1 - 1.5*IQR. These points are likely:

Accepted Answer

Outliers. The 1.5×IQR rule is the standard Tukey fence for identifying outliers in exploratory data analysis. Any value below Q1 − 1.5×IQR (or above Q3 + 1.5×IQR) is flagged as a potential outlier. Since the question states points fall below Q1 − 1.5×IQR, they are by definition outliers.

Answer

Normal variations within the distribution

Answer

The mode of the dataset

Answer

The median of the dataset

Question 9

A data analyst is comparing the average test scores of students who attended a tutoring program versus those who did not. Which statistical test is most appropriate for determining if there is a significant difference between the means of these two independent groups?

Accepted Answer

Two-sample t-test. The independent samples t-test is used to compare the means of two independent groups.

Answer

Paired t-test

Answer

Chi-square test

Answer

ANOVA

Question 10

Which of the following is an example of unstructured data?

Accepted Answer

An image file. An image file is unstructured data because it has no predefined data model or schema — its content is raw pixel data that requires specialized processing (e.g., computer vision) to extract meaning. This contrasts with structured formats that organize data into rows, columns, or key-value pairs.

Answer

A JSON file

Answer

A relational database table

Answer

A CSV file with rows and columns

Question 11

Which of the following is an example of qualitative data?

Accepted Answer

Customer feedback comments. Customer feedback comments are qualitative data because they consist of non-numerical, descriptive text that captures opinions, sentiments, or experiences. Unlike quantitative data, which can be measured or counted, qualitative data is categorical and often requires thematic analysis to derive insights.

Answer

Stock price

Answer

Number of website visitors

Answer

Product weight in grams

Question 12

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.

Answer

Accuracy

Answer

F1 score

Answer

Precision

Question 13

A data analyst wants to combine first_name and last_name columns into a single full_name column in a SQL query. Which string function should be used?

Accepted Answer

CONCAT(). CONCAT() joins two or more strings together.

Answer

UPPER()

Answer

LENGTH()

Answer

SUBSTRING()

Question 14

A data analyst is preparing a presentation on customer churn. The audience consists of both technical and non-technical stakeholders. Which visualization approach is most effective?

Accepted Answer

A simple bar chart showing churn rate by segment.. A simple bar chart showing churn rate by segment is most effective because it directly communicates the key metric (churn rate) across categorical segments (e.g., customer demographics or plan types) in a format that is immediately understandable to both technical and non-technical stakeholders. Bar charts excel at comparing discrete categories without requiring statistical literacy, making them ideal for mixed audiences in a presentation context.

Answer

A box plot showing distribution of churn.

Answer

A heatmap showing correlation of churn factors.

Answer

A scatter plot with multiple variables.

Question 15

Match each database concept to its definition.

Accepted Answer

Primary Key: Uniquely identifies each record in a table.. Primary keys uniquely identify records, foreign keys link tables, indexes speed retrieval, and normalization reduces redundancy. Common confusions include swapping primary and foreign key definitions.

Answer

Primary Key: A field that links to the primary key of another table.

Answer

Foreign Key: Uniquely identifies each record in a table.