DS0-001 Data Querying And Analysis Practice Test 3 — 15 Questions

Question 1

When performing sentiment analysis on customer feedback, which preprocessing step is most critical to ensure words like 'Running' and 'run' are treated as the same base concept?

Accepted Answer

Lemmatization. Lemmatization or stemming reduces words to their root or base form.

Answer

Stop-word removal

Answer

Tokenization

Answer

Feature scaling

Question 2

Which dashboarding practice helps prevent users from being overwhelmed by too much data at once?

Accepted Answer

Using interactive filters. Interactive filters let users narrow the dataset to what is relevant to them, reducing cognitive overload and preventing the dashboard from displaying every metric at once. This is a core dashboard design principle: progressive disclosure through filtering, slicing, and drill-down. It keeps the initial view clean while still giving access to detail on demand.

Answer

Removing all tooltips

Answer

Adding more charts per page

Answer

Increasing font size

Question 3

What is the benefit of using a 'Dashboard' over a static report?

Accepted Answer

Dynamic exploration and interactivity. A dashboard's defining advantage over a static report is interactivity: users can filter, drill down, change time ranges, and pivot dimensions in real time without requesting a new report. This dynamic exploration lets stakeholders answer follow-up questions on the spot, whereas a static report presents a fixed snapshot that must be regenerated for any variation.

Answer

Simplified data security

Answer

Lower storage requirements

Answer

Easier to print

Question 4

What does the 'P' in the acronym 'KPI' stand for?

Accepted Answer

Performance. KPI stands for Key Performance Indicator, where the 'P' represents Performance. A KPI is a quantifiable measure used to evaluate the success of an organization, project, or activity against strategic objectives. The 'Performance' element emphasizes that KPIs measure how well something is performing relative to a target.

Answer

Product

Answer

Process

Answer

Planning

Question 5

When analyzing JSON data stored in a relational database using SQL, how do you typically access nested key-value pairs?

Accepted Answer

Using path-based operators. Path-based operators are the correct approach because JSON stored in relational databases (e.g., PostgreSQL's jsonb, MySQL's JSON, SQL Server's JSON functions) is accessed using path expressions that navigate nested keys and array indices. Examples include PostgreSQL's -> and ->> operators, MySQL's JSON_EXTRACT() and -> syntax, and SQL Server's JSON_VALUE() with '$.key.subkey' paths. These operators let you drill into nested structures without transforming the data.

Answer

Converting to XML first

Answer

Using the SELECT * command

Answer

Using standard JOINs

Question 6

When creating an ETL pipeline, which stage involves validating that the data meets business rules (e.g., age must be positive)?

Accepted Answer

Transformation. The Transformation stage is where data is cleansed, standardized, and validated against business rules before loading into the target system. Checking that age is positive, that dates fall within valid ranges, or that codes match a reference list are all transformation-layer validations. Extraction only pulls raw data, and Loading only writes it to the destination.

Answer

Extraction

Answer

Loading

Answer

Reporting

Question 7

When normalizing a database, what is the primary goal of moving data into a third normal form (3NF)?

Accepted Answer

Remove transitive dependencies. Third normal form (3NF) builds on 2NF by eliminating transitive dependencies — non-key attributes that depend on other non-key attributes rather than directly on the primary key. The rule is that every non-key column must depend on 'the key, the whole key, and nothing but the key.' Removing transitive dependencies prevents update anomalies where changing one fact forces changes in unrelated rows, ensuring each fact is stored in exactly one place.

Answer

Enable multi-dimensional analysis

Answer

Increase data redundancy

Answer

Increase read speed

Answer

Combine multiple tables

Question 8

When troubleshooting a performance bottleneck in a visualization, which action is most effective if the data source is a large SQL view?

Accepted Answer

Materializing the view. Materializing the view (creating a physical table with indexes) or optimizing the view's underlying query is the most effective way to resolve performance issues.

Answer

Changing the chart color

Answer

Increasing the number of filters

Answer

Using a different visualization software

Question 9

When using Python's 'Matplotlib' library, which function is used to add a new figure to the canvas?

Accepted Answer

plt.figure(). In Matplotlib, the plt.figure() function is used to create a new figure object, which serves as the canvas for plotting. Calling plt.figure() adds a new figure to the current pyplot state and returns a Figure instance that can be populated with axes and plots. Without calling plt.figure(), plotting functions like plt.plot() will draw on the current figure, or create one implicitly if none exists.

Answer

plt.axis()

Answer

plt.plot()

Answer

plt.show()

Question 10

Which SQL command is used to add new records to an existing table?

Accepted Answer

INSERT. INSERT INTO is the standard command for adding new rows to a database table.

Answer

UPDATE

Answer

SELECT

Answer

ALTER

Question 11

In the context of data quality, what does 'Completeness' refer to?

Accepted Answer

The absence of missing values. In data quality, Completeness refers to the degree to which all required data values are present — i.e., the absence of missing values in a dataset. It measures whether records have all expected attributes populated, not whether those values are correct or consistent.

Answer

The consistency of data across sources

Answer

The timeliness of the data

Answer

The accuracy of values

Question 12

Which of the following is a limitation of the 'Star Schema' design compared to a fully normalized schema?

Accepted Answer

Increased data redundancy. A star schema deliberately denormalizes dimension tables to optimize read performance, which inherently duplicates attribute values across dimension rows and across fact-to-dimension relationships. This redundancy is the accepted trade-off for simpler, faster analytical queries. A fully normalized schema (3NF and beyond) eliminates such duplication by splitting data into many related tables.

Answer

Inability to store large data volumes

Answer

Slower query performance

Answer

Difficulty in writing joins

Question 13

Which TWO of the following are common types of data visualizations used to represent trends over time?

Accepted Answer

Line Chart. A Line Chart (C) is correct because it plots data points connected by lines along a continuous time axis, making it the standard visualization for showing trends, patterns, and changes over time. An Area Chart (D) is also correct because it is essentially a line chart with the area beneath the line filled in, and it likewise uses a continuous time-based axis to represent trends over time (often emphasizing magnitude or cumulative volume). A Histogram (A) is not correct here because it displays the frequency distribution of continuous data across bins rather than a time series. A Scatter Plot (B) is not correct because it shows the relationship or correlation between two variables as individual points, not a trend over time. A Pie Chart (E) is not correct because it represents parts of a whole as proportional slices at a single point in time, not changes over time.

Answer

Histogram

Answer

Scatter Plot

Answer

Pie Chart

Question 14

Which THREE of the following are components of a standard SQL SELECT statement?

Accepted Answer

WHERE. The WHERE clause (C) is a core component of a standard SQL SELECT statement because it filters the rows returned by the query according to a specified predicate. The FROM clause (D) is also essential, as it specifies the table or tables from which the data is retrieved. The GROUP BY clause (E) is a standard SELECT component that aggregates rows sharing common values into groups, typically used with aggregate functions such as COUNT, SUM, or AVG. By contrast, DROP (A) is a DDL statement used to delete database objects like tables or views, and GRANT (B) is a DCL statement used to assign privileges to users, so neither belongs inside a SELECT statement.

Answer

DROP

Answer

GRANT

Question 15

Which THREE of the following SQL aggregate functions are commonly supported in most relational databases?

Accepted Answer

SUM. SUM, COUNT, and AVG are fundamental aggregate functions supported across virtually all SQL standards.

Answer

CONCAT

Answer

SUBSTR