DA0-002 Practice Test 19 — 15 Questions

Question 1

A data analyst is pulling data from a production database for a report. The database contains customer orders with a column 'order_date'. The analyst notices that some orders have dates in the future. Which data quality issue does this represent?

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

Accepted Answer

Violation of business rules. Future order dates violate a business rule that order_date must be in the past or present. This is a classic data integrity issue where the data does not conform to domain-specific constraints, such as 'order_date <= CURRENT_DATE'. The analyst should flag this as a violation of business rules, not a data type or consistency problem.

Answer

Invalid data type

Answer

Inconsistent data

Answer

Missing data

Question 2

A data analyst is tasked with visualizing the distribution of customer ages across different regions. The dataset contains outliers. Which chart type best displays the distribution and highlights outliers?

Accepted Answer

Box plot. A box plot is purpose-built to display distribution summary statistics—median, quartiles, and whiskers—and explicitly marks outliers as individual points beyond the whiskers. This makes it the best choice when the requirement is to show distribution across regions while highlighting outliers. Grouping box plots by region allows direct comparison of age distributions.

Answer

Violin plot

Answer

Histogram

Answer

Bar chart

Question 3

Refer to the exhibit. Which type of data is the field "region"?

Accepted Answer

Qualitative. The field 'region' contains categorical labels (e.g., 'North', 'South', 'East', 'West') that represent distinct groups or categories, not numerical measurements. Qualitative data (also called categorical data) describes attributes or characteristics that can be named but not meaningfully ordered or measured on a numeric scale. Since 'region' assigns a name to a geographic area without any inherent numeric value or order, it is a classic example of qualitative data.

Answer

Continuous

Answer

Quantitative

Answer

Discrete

Question 4

A data analyst is cleaning a dataset and finds that a numeric field has several missing values. The variable is normally distributed. Which imputation method is most appropriate?

Accepted Answer

Mean imputation. For a normally distributed numeric variable, mean imputation is the most appropriate method because the mean is the central tendency that best represents the typical value in a symmetric distribution. Replacing missing values with the mean preserves the overall mean of the variable and is statistically sound when the data is normally distributed. Other methods like median or mode are better for skewed or categorical data, respectively.

Answer

Median imputation

Answer

Mode imputation

Answer

Forward-fill

Question 5

Which stage of the data lifecycle involves converting raw data into a usable format, such as cleaning or validating?

Accepted Answer

Processing. Processing is the stage where raw data is transformed into a usable format through cleaning, validation, normalization, or aggregation. This step ensures data quality and consistency before analysis or storage, directly matching the question's description.

Answer

Archival

Answer

Ingestion

Answer

Storage

Question 6

A data analyst is cleaning a dataset with missing values in a time series of daily temperatures. The missing values occur sporadically. Which imputation method is most appropriate to maintain the temporal trend?

Accepted Answer

Interpolation. Interpolation estimates missing values by using the values immediately before and after the gap, producing a smooth curve that preserves the temporal trend of a time series. For sporadically missing daily temperatures, linear interpolation between neighboring days is the most faithful reconstruction. It respects the ordered, continuous nature of the data better than simple fill methods.

Answer

Forward-fill

Answer

Mean imputation

Answer

Median imputation

Question 7

A company needs to store raw data from IoT sensors for future machine learning projects. The data is expected to be massive and in various formats. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake stores raw data in its native format without predefined schema, ideal for large volumes of varied data for future use.

Answer

Data mart

Answer

Data warehouse

Answer

Relational database

Question 8

A data analyst uses a CTE to simplify a complex query. Which keyword is used to define a CTE?

Accepted Answer

WITH. A Common Table Expression (CTE) is defined using the WITH keyword followed by a name and an AS clause containing the subquery. WITH precedes the main SELECT and can define one or more named CTEs separated by commas.

Answer

DEFINE

Answer

CTE

Answer

DECLARE

Question 9

A data engineer is designing a system to store raw sensor data from thousands of IoT devices. The data will be used later for various analytics projects, but the schema is not yet defined. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake is designed to store raw, unprocessed data in its native format without requiring a predefined schema, which is exactly what is needed for IoT sensor data whose schema is not yet defined. It supports schema-on-read, allowing analytics projects to interpret the data later as requirements evolve. This makes it the most appropriate choice for storing diverse, high-volume raw data for future analytics.

Answer

Data mart

Answer

Data warehouse

Answer

Relational database

Question 10

Refer to the exhibit. A stakeholder complains that the line chart exaggerates the changes in sales. What is the most likely cause?

Accepted Answer

The y-axis does not start at zero. A line chart can exaggerate changes if the y-axis does not start at zero. By truncating the y-axis (e.g., starting at 80 instead of 0), small fluctuations appear as large peaks and valleys, misleading viewers. This is a common data visualization pitfall that distorts the perception of magnitude.

Answer

There are too few data points

Answer

The data labels are incorrect

Answer

The chart type should be a bar chart

Question 11

A dataset contains the ages of 100 customers. The analyst wants to transform the ages to a 0-1 range for use in a distance-based algorithm. Which technique should be used?

Accepted Answer

Min-max normalization. Min-max normalization transforms features to a fixed range, typically 0 to 1, by subtracting the minimum and dividing by the range. This is exactly what is needed to scale ages to a 0-1 range for distance-based algorithms.

Answer

Square root transformation

Answer

Log transformation

Answer

Z-score normalization

Question 12

A hospital's data team is cataloging its data assets. They need to classify the blood type field stored for each patient (e.g., A+, O-). Which data type classification best describes this field?

Accepted Answer

Nominal. Blood type is a categorical label that identifies which of the recognized blood groups a patient belongs to. There is no inherent order among A+, O-, AB+, and B-, and no arithmetic can be meaningfully performed on these values. Nominal classification is the only correct choice because it groups data into unordered, distinct categories.

Answer

Interval

Answer

Ordinal

Answer

Ratio

Question 13

An analyst is presenting a recommendation to increase marketing spend. Which statement best follows the data-driven recommendation structure (evidence → insight → recommendation → expected impact)?

Accepted Answer

Based on a 5% lift in sales from previous campaigns, we recommend a 10% increase in marketing spend, expecting a 7% revenue growth.. The correct structure provides evidence, insight derived from it, a recommendation, and the expected impact.

Answer

A 10% increase in marketing spend is recommended because we have budget.

Answer

The data shows sales are up, so we should spend more on marketing.

Answer

We should increase marketing spend by 10% because it might boost sales.

Question 14

Which THREE of the following are common characteristics of unstructured data?

Accepted Answer

Often stored in NoSQL databases or data lakes. Option B is correct because unstructured data, such as documents, media files, and logs, is commonly stored in NoSQL databases (e.g., MongoDB, Cassandra) or data lakes (e.g., Amazon S3, Azure Data Lake) that do not require a fixed relational schema. Option C is correct because unstructured data encompasses heterogeneous formats including free text, images, audio, and video, which cannot be easily decomposed into rows and columns. Option E is correct because unstructured data by definition lacks a predefined schema, meaning there is no fixed data model or rigid structure enforced at write time. Option A is incorrect because SQL querying relies on structured, tabular schemas, which unstructured data does not provide. Option D is incorrect because relational tables are the storage model for structured data, not unstructured data.

Answer

Easily queried using SQL

Answer

Stored in relational tables

Question 15

A financial analyst is working with a dataset of stock prices. The dataset contains a column 'Price' that records the closing price of each stock in USD. The analyst wants to calculate the percentage change in price from the previous day for each stock. Which data type is most appropriate for the 'Price' column?

Accepted Answer

Float. Stock prices require decimal precision to accurately represent fractional dollar amounts and to perform arithmetic for percentage changes. Float data types store real numbers with decimals, making them the correct choice. Integer, string, and Boolean types cannot properly capture or compute with the continuous numeric values needed for this analysis.

Answer

Integer

Answer

String

Answer

Boolean