DA0-002 Practice Test 14 — 15 Questions

Question 1

A data analyst is summarizing the central tendency of a dataset with extreme outliers. Which measure is most robust to outliers?

Accepted Answer

Median. The median is the middle value when data is ordered, so extreme outliers (whether very high or very low) do not affect its position. Unlike the mean, which incorporates every value and can be pulled strongly by outliers, the median remains stable. Therefore, for a dataset with extreme outliers, the median is the most robust measure of central tendency.

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Standard deviation

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Mean

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Range

Question 2

Match each data quality dimension to its description.

Accepted Answer

Accuracy: Data correctly reflects real-world values. The correct matches are: Accuracy - data correctly reflects real-world values; Completeness - all required data is present; Consistency - data values are the same across systems; Timeliness - data is available when needed. Common confusions include mixing timeliness with accuracy and consistency with completeness.

Answer

Accuracy: Data is available when needed

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Completeness: Data values are the same across different systems

Question 3

During data acquisition, a data engineer uses a tool to extract data from a source system incrementally based on a timestamp column. Which method is being used?

Accepted Answer

Change data capture (CDC). The method being used is change data capture (CDC), specifically timestamp-based CDC. CDC is a technique used to identify and capture changes made to data in a source system since the last extraction, often by using a timestamp column (e.g., last_modified) to select only rows that have changed. This allows for incremental extraction, reducing load and improving efficiency.

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Snapshot extraction

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Full extraction

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Manual extraction

Question 4

You are a data analyst for a logistics company. The company has a fleet of delivery trucks and tracks performance metrics including delivery time, fuel consumption, and distance traveled. Management wants a dashboard to monitor driver efficiency and identify underperforming drivers. You have access to a dataset with columns: DriverID, Date, RouteID, Distance (miles), FuelUsed (gallons), DeliveryTime (minutes). The dataset contains 10,000 records from the past year. You need to create a visualization that allows management to quickly compare the average fuel efficiency (miles per gallon) of drivers and also see how consistent each driver's efficiency is. Which of the following approaches is the best course of action?

Accepted Answer

Create a box plot grouped by DriverID showing the distribution of MPG for each driver.. A box plot grouped by DriverID is the best choice because it simultaneously shows the central tendency (median MPG) and the spread (interquartile range and outliers) of each driver's fuel efficiency. This allows management to quickly compare average efficiency across drivers while also assessing consistency—drivers with narrow boxes are more consistent, while those with wide boxes or many outliers are erratic. The other options either fail to show distribution (bar chart, line chart) or require manual interpretation of consistency (scatter plot).

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Create a line chart with Date on the x-axis and MPG on the y-axis, with separate lines for each driver.

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Create a scatter plot with Distance on the x-axis and FuelUsed on the y-axis, color-coded by DriverID.

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Create a bar chart showing the average MPG for each driver.

Question 5

A data analyst is using pandas to read a CSV file named 'sales.csv'. Which line of code correctly reads the file into a DataFrame?

Accepted Answer

import pandas as pd; df = pd.read_csv('sales.csv'). The correct pandas idiom is pd.read_csv('sales.csv'), which returns a DataFrame. pandas is conventionally imported as pd, and read_csv is the dedicated CSV parser that handles delimiters, headers, dtypes, and encoding. This is the canonical one-liner every pandas user writes.

Answer

import csv; df = csv.read('sales.csv')

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import pandas as pd; df = pd.read('sales.csv')

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import numpy as np; df = np.read_csv('sales.csv')

Question 6

Which TWO are examples of leading indicators in a business context? (Select two.)

Accepted Answer

Customer engagement score. Leading indicators are forward-looking metrics that predict future performance, and option C (customer engagement score) qualifies because engagement levels today tend to forecast future retention, loyalty, and purchasing behavior. Option D (number of qualified leads) is also a leading indicator since a healthy pipeline of qualified prospects predicts future sales and revenue before those deals close. By contrast, option A (employee turnover rate) is a lagging indicator because it measures past attrition that has already occurred, and options B (net profit margin) and E (monthly revenue) are lagging outcome metrics that report financial results after the fact rather than predicting them.

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Employee turnover rate

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Net profit margin

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Monthly revenue

Question 7

An organization is implementing a data lake to store raw data from various sources. Which THREE characteristics are typically associated with a data lake compared to a data warehouse?

Accepted Answer

Supports batch and real-time processing. A is correct because a data lake is designed to handle both batch and real-time/streaming ingestion and processing, unlike a traditional data warehouse that is primarily optimized for batch ETL workloads. B is correct because a data lake stores data in its native/raw format (e.g., JSON, Parquet, CSV, images, logs) without forcing an upfront conversion. C is correct because a data lake applies schema-on-read, meaning the schema is defined when the data is queried rather than when it is written. D is incorrect because data lakes support structured, semi-structured, and unstructured data, not only structured data. E is incorrect because a data lake typically loads raw data as-is and defers transformation until read/query time, whereas a data warehouse usually requires transformation before loading.

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Supports only structured data

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Requires data transformation before loading

Question 8

A company is merging two databases from different departments. In Database A, customer IDs are integers. In Database B, customer IDs are alphanumeric strings. To merge, the data analyst must reconcile these differences. Which step should be taken first?

Accepted Answer

Perform data profiling to understand the ID formats and relationships. Data profiling is the essential first step before any transformation or mapping. It allows the analyst to examine the actual formats, patterns, and relationships in both ID columns (e.g., whether Database B's alphanumeric IDs contain embedded numeric sequences or consistent prefixes). Without profiling, any conversion or mapping would be based on assumptions that could lead to data loss or incorrect merges.

Answer

Drop the ID column and use a surrogate key

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Convert all IDs to integers using CAST

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Create a mapping table based on the first character

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.

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Quantitative

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Ordinal

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Discrete

Question 10

A data analyst is performing EDA on a dataset with numerical features. Which methods are appropriate for identifying outliers? (Select TWO).

Accepted Answer

Z-score method. The Z-score method (C) is correct because it quantifies how many standard deviations a value lies from the mean, so observations with |z| above a threshold such as 3 (or 2.5) are flagged as outliers in numerical features. The Interquartile range (IQR) method (E) is also correct because it defines outliers as values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR, making it a robust, distribution-agnostic detection technique. Mean imputation (A) is not an outlier-detection method at all; it is a missing-value handling technique that replaces NaNs with the column mean. The Pearson correlation coefficient (B) measures the linear relationship between two variables, not the extremeness of individual values, so it cannot identify outliers. Standard deviation alone (D) only describes data spread and, without a rule like the Z-score, does not flag specific observations as outliers.

Answer

Mean imputation

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Pearson correlation coefficient

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Standard deviation alone

Question 11

Which TWO of the following are considered structured data?

Accepted Answer

A relational database table. A relational database table stores data in a predefined schema of rows and columns, where each column has a fixed data type. This rigid structure allows for efficient querying, indexing, and relational operations, making it a classic example of structured data.

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A PDF report with free-form text

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A JPEG image of a product

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A JSON file with nested key-value pairs

Question 12

A retail company's data analytics team needs to acquire point-of-sale (POS) transaction data from 200 stores daily. Each store sends a CSV file via email at the end of the day. The files often arrive late, have inconsistent column names (e.g., "StoreID", "Store_ID", "store_id"), and occasionally contain corrupted rows. The team manually processes these files, leading to frequent errors and delays. The company wants to automate the acquisition process to ensure data is available by 9 AM the next business day with high quality. Which approach best addresses these issues?

Accepted Answer

Create a script to automatically download email attachments, validate and standardize columns, and flag corrupted rows for review. It directly addresses all three issues: automating the retrieval of email attachments (handling late arrivals), standardizing inconsistent column names via a script (e.g., mapping 'StoreID', 'Store_ID', 'store_id' to a canonical schema), and implementing validation logic to flag corrupted rows for manual review. This approach ensures data is processed reliably by 9 AM without manual intervention, meeting the automation and quality requirements.

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Hire a data entry contractor to manually check and re-enter data

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Ask stores to use a standardized web form to enter data directly into a cloud database

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Implement a VPN so stores can connect to the central database and write transactions in real time

Question 13

A data analyst is performing data profiling on a customer table. Which metric would best help identify missing values in the 'phone' column?

Accepted Answer

Null count. The null count metric directly measures the number of missing (NULL) values in a column, which is exactly what the analyst needs to identify missing phone numbers. Data profiling tools report null count per column as a standard completeness metric. Other metrics like cardinality or mean do not reveal missingness.

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Cardinality

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Mean

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Row count

Question 14

A data scientist is building a machine learning model to predict customer churn. The dataset includes both numerical features (age, income) and categorical features (gender, marital status). Which data concept describes the process of converting categorical features into numerical values that can be used by the algorithm?

Accepted Answer

Encoding. Encoding is the correct data concept because it transforms categorical features (like gender and marital status) into numerical representations (e.g., one-hot encoding, label encoding) that machine learning algorithms can process. Unlike feature scaling or dimensionality reduction, encoding directly addresses the incompatibility of non-numeric data with mathematical model operations.

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Data sampling

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Feature scaling

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Dimensionality reduction

Question 15

An e-commerce company uses a star schema for its data warehouse. The fact table 'sales_fact' contains foreign keys to dimension tables: customer_dim, product_dim, time_dim, and store_dim. A business user wants to know the total sales for each product category in the last month. Which join operation is required to retrieve this data?

Accepted Answer

Inner join between fact table and dimension tables. To retrieve total sales for each product category, you need to join the fact table with the product dimension table to map product keys to categories, and with the time dimension table to filter on the last month. An inner join is correct because it returns only rows where matching keys exist in both tables, which is the standard approach for star-schema queries where all required dimension attributes are present. This ensures that only valid sales transactions with corresponding product and time entries are included in the aggregation.

Answer

Self-join on the fact table

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Cross join between fact and dimension tables

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Left outer join between fact and dimension tables