The primary requirement is to visualize the "composition of total sales amounts" across different channels. A pie chart is the most suitable visualization for this purpose as it is specifically designed to show part-to-whole relationships. Each slice of the pie would represent a sales channel (Digital, Store, Online), and the size of the slice would be proportional to its contribution to the total sales, clearly illustrating the composition.

A. Pivot table: A pivot table is a tool for data summarization in a tabular format, not a graphical visualization designed to show proportional composition.

C. KPI card: A Key Performance Indicator (KPI) card is used to display a single, aggregate metric (e.g., total sales), not the breakdown or composition of that metric across categories.

D. Box plot: A box plot is used to display the statistical distribution of numerical data (e.g., median, quartiles, outliers), not the proportional contribution of categories to a total.

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1. Microsoft Power BI Documentation. (n.d.). Pie charts in Power BI. Microsoft Docs. Retrieved from the official Power BI documentation on visualization types. In the section "When to use a pie chart

" it states

"Pie charts are a great choice... To show proportions." This directly aligns with the question's need to show the composition of total sales.

2. Wilke

C. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media. In Chapter 10

"Visualizing proportions

" Section 10.1

"Pie charts

" the author states

"A pie chart represents the whole of a given quantity as a circle

and the parts as wedges of that circle. The parts are sized by their relative contribution to the whole." This is a standard use case taught in data visualization curricula at institutions like the University of Texas at Austin

where the author is a professor.

3. University of Washington. (n.d.). Data Visualization: Basic Principles. Course materials for DATA 512: Data Visualization. The course materials explain that pie charts are used to show the "relative shares of categories that sum to a whole

" which is precisely the task of showing the composition of sales channels.

The most efficient and standard method for this task is to work with structured data formats that Python's data analysis libraries, like pandas, are optimized for. Web APIs commonly return data in JSON (JavaScript Object Notation) format. This structured format can be directly and easily parsed into a pandas DataFrame, preserving data types and relationships. Similarly, data queried from a database can be loaded directly into a DataFrame. Once both data sources are represented as DataFrames, pandas provides powerful and optimized merge or join functions to combine them based on common keys. This approach minimizes intermediate conversion steps and avoids the loss of data type information.

A. varchar is a database-specific data type, not a universal data interchange format. Converting all data to this string-like type would unnecessarily discard valuable numeric and date-type information.

C. Converting structured API and database data to a flat TXT file is an inefficient intermediate step that adds complexity and risks data integrity issues during parsing.

D. Converting all data to a generic string format is a poor practice as it loses essential data type information (e.g., integers, floats), making subsequent numerical analysis and joins difficult.

1. Pandas Official Documentation

IO Tools (JSON): The documentation explicitly details the pandas.readjson function for converting a JSON string directly into a pandas DataFrame. This is the standard method for handling API data.

Source: Pandas Development Team. (2023). pandas-docs: IO Tools (Text

CSV

HDF5

…). Section: JSON. Retrieved from https://pandas.pydata.org/pandas-docs/stable/userguide/io.html#json

2. Pandas Official Documentation

IO Tools (SQL): The documentation shows the pandas.readsql function

which is designed to read the result of a SQL query directly into a DataFrame

preserving data types from the database where possible.

Source: Pandas Development Team. (2023). pandas-docs: IO Tools (Text

CSV

HDF5

…). Section: SQL queries. Retrieved from https://pandas.pydata.org/pandas-docs/stable/userguide/io.html#sql-queries

3. University of Illinois Urbana-Champaign

Courseware: In the "Data Visualization" course materials

the standard workflow for handling web data is demonstrated. It involves making an API request

parsing the resulting JSON response

and loading it into a data structure like a pandas DataFrame for analysis.

Source: University of Illinois Urbana-Champaign. (2022). CS 307: Working with APIs and JSON. Week 3 Notes. Retrieved from https://uiuc-ischool-dataviz.github.io/spring2022/week03/workingwithapisandjson.html (Specifically

the section on parsing JSON and creating DataFrames).

The analyst has performed initial, internal troubleshooting steps: using print statements for debugging, conducting a peer review, and verifying software compatibility and versioning. When these methods fail, the next logical step in a standard troubleshooting methodology is to broaden the search for a solution by consulting external resources. Researching the issue online provides access to official documentation, knowledge bases, and community forums where similar problems and their resolutions are often documented. This step is a crucial part of due diligence before escalating the issue or resorting to workarounds.

A. This is a temporary workaround, not a troubleshooting step to solve the current problem. It avoids the issue rather than addressing it.

B. It is premature to declare a software bug and contact IT. The analyst should first exhaust self-service resources, as the issue might be a known problem with a documented solution.

C. Escalating to a manager is inappropriate for a technical problem at this stage. This step should only be taken after standard technical troubleshooting and escalation paths have been exhausted.

1. Sommerville

I. (2016). Software Engineering (10th ed.). Pearson.

In Chapter 8

Section 8.4 on "Debugging

" the text describes the process of locating and repairing defects. A key part of locating a defect is information gathering. When initial hypotheses fail

a developer must gather more information

which in a modern context includes searching for known issues in external knowledge bases before escalating. This aligns with researching the problem online.

2. Zeller

A. (2009). Why Programs Fail: A Guide to Systematic Debugging (2nd ed.). Morgan Kaufmann.

Chapter 1

"Isolating Failure Causes

" emphasizes a scientific method for debugging: observe

hypothesize

test

and repeat. When a programmer's own hypotheses are exhausted (as in the scenario)

the next step is to gather more data or see if others have already solved the problem. The book implicitly supports consulting external documentation and community knowledge as part of the observation/information-gathering phase.

3. Ko

A. J.

& Myers

B. A. (2004). A framework and methodology for studying the causes of software errors in programming systems. Journal of Visual Languages & Computing

15(1)

41-84. https://doi.org/10.1016/j.jvlc.2003.07.004

This paper discusses the cognitive processes of debugging. It highlights that a major part of resolving errors involves "information seeking." After failing to find information through code inspection (as the analyst did)

a programmer naturally progresses to external information sources like documentation or online communities. This supports online research as the next logical action.

The configuration aligns data visualization best practices with the dashboard's requirements.

• The "Power Outages Enterprise-wide" title matches the director's need to monitor the "entire state."
• For "County Outages," a map is the most effective visualization for displaying data distributed across a geographic area.
• For "Status," the grouped bar chart is ideal as it allows for the comparison of categorical data (Closed, In Progress, Reported) across multiple groups (Counties).
• For "Number of Outages for the Quarter," the pie chart is used to represent a part-to-whole relationship, showing how the total quarterly outages are distributed among the counties.

Microsoft. (n.d.). Basic concepts for designers in the Power BI service. Microsoft Learn. Retrieved November 7, 2025.

Reference: In the section "Visualizations in Power BI reports," Microsoft documentation guides the use of "Maps" for spatial data, "Bar charts" for comparing values across categories, and "Pie charts" for showing the relationship of parts to a whole. This supports the selection of the map for counties, the bar chart for status, and the pie chart for the quarterly breakdown.

University of Washington. (n.d.). Data Visualization: A Guide to Good Practice. UW Libraries.

Reference: This guide discusses chart selection, noting that "Chloropleth maps are used to show data aggregated over geographical regions" (supporting the choice for "County Outages") and "Bar charts are used to compare values across categories" (supporting the choice for "Status").

IEEE. (2011). Information Visualization. In VIS '11: Proceedings of the IEEE VIS 2011.

Reference: Academic principles of information visualization emphasize selecting a visual encoding that matches the user's task. The task "identify where... resources should be allocated" (geographic) is directly supported by a map. The task of monitoring "status" (comparison) is supported by a bar chart.

The SQL UNION operator is designed to combine the result sets of two or more SELECT statements into a single result set. A key characteristic of the UNION operator is that it automatically removes duplicate rows from the final result. The analyst's task is to merge records from the "Employees" and "Managers" tables and eliminate any duplicates between them. The statement SELECT FROM Employees UNION SELECT FROM Managers correctly performs this operation by vertically appending the two tables and filtering out any identical rows, thus achieving the desired outcome.

A. UNION ALL combines result sets but includes all rows, including duplicates, which fails to meet the requirement to remove them.

C. The JOIN syntax is invalid, and its purpose is to combine columns from different tables based on a related key, not to combine rows.

D. CROSS JOIN creates a Cartesian product, pairing every row from the first table with every row from the second, which is not the required operation.

1. PostgreSQL 16 Documentation. (n.d.). 7.4. Combining Queries. PostgreSQL. Retrieved from https://www.postgresql.org/docs/16/queries-union.html. In Section 7.4

the documentation states

"The UNION operator computes the set union of the rows returned by the involved SELECT statements. A row is in the set union of two result sets if it appears in at least one of the result sets. ... Duplicate rows are eliminated from the result..."

2. Oracle Database SQL Language Reference

23c. (n.d.). Set Operators. Oracle. Retrieved from https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Set-Operators.html#GUID-B0F24731-B6B5-465F-940E-25D617553A0E. The documentation specifies

"The UNION operator returns the distinct rows from the result sets of the two queries." It contrasts this with UNION ALL

which "does not eliminate duplicate rows."

3. Ullman

J. D.

& Widom

J. (n.d.). CS145 Introduction to Databases

Lecture Notes: SQL 2. Stanford University. Retrieved from https://web.stanford.edu/class/cs145/notes/SQL-2.pdf. On page 2

under "Union

Intersection

Difference

" the notes explain that (SELECT ... FROM ... WHERE ...) UNION (SELECT ... FROM ... WHERE ...) computes the set union and that "UNION removes duplicates."

A scatter plot is the most suitable visualization for examining the relationship or correlation between two continuous numerical variables. In this scenario, 'age' and 'income' are both continuous variables. Each employee can be represented as a point on the graph, with their age plotted on the x-axis and their income on the y-axis. This visual representation allows the human resources department to easily identify patterns, trends (e.g., does income increase with age?), and outliers within the dataset.

B. Area plot: An area plot is typically used to show how a quantitative value changes over a continuous dimension, such as time, and is not suitable for correlating two distinct variables.

C. Bar chart: A bar chart is designed to compare values across discrete categories, not to illustrate the relationship between two continuous variables like age and income.

D. Pie chart: A pie chart is used to display the proportional composition of a single categorical variable, representing parts of a whole, and cannot show a relationship between two variables.

1. Wilke

C. O. (2019). Fundamentals of Data Visualization. O'Reilly Media

Inc. In Chapter 12

"Visualizing associations among two or more quantitative variables

" the text states

"The standard visualization for an association between two quantitative variables is the scatterplot." The chapter proceeds to provide examples using this chart type.

2. University of Virginia Library

Research Data Services. (n.d.). Data Visualization: The Scatterplot. Retrieved from the University of Virginia Library website. The guide specifies

"A scatterplot is a type of data visualization that shows the relationship between two numerical variables... Scatterplots are used to observe and show relationships between two numeric variables."

3. Healy

K. (2018). Data Visualization: A Practical Introduction. Princeton University Press. In Chapter 3

"Make a Plot

" the section on scatterplots explains

"Scatterplots are the default choice for visualizing the relationship between two continuous variables" (p. 56).

The dataset contains both "dates of birth" and "employees’ birth months." The birth month can be directly derived or calculated from the date of birth. Storing data that can be derived from other fields within the same record is a form of data redundancy. This practice is inefficient as it increases storage needs and, more importantly, creates a risk of inconsistency. For example, if the date of birth is corrected, the birth month field might not be updated, leading to conflicting information within the same record. Identifying and resolving such redundancy is a fundamental step in data cleaning and governance.

B. Equivalence: This refers to different data values representing the same real-world entity (e.g., 'St.' and 'Street'), which is not the issue here.

C. Parallel: This term is not a standard classification for data inconsistency; it typically relates to system architecture or data processing methods.

D. Duplication: This usually refers to entire records (rows) being repeated in a dataset, not redundant information within a single record.

---

1. Elmasri

R.

& Navathe

S. B. (2017). Fundamentals of Database Systems (7th ed.). Pearson. In Chapter 15

"Basics of Functional Dependencies and Normalization for Relational Databases

" the authors discuss how storing derivable values (e.g.

a person's age from their birth date) introduces redundancy and can lead to update anomalies (Section 15.2.1). The presence of both 'date of birth' and 'birth month' is a direct example of this principle.

2. DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Technics Publications. Chapter 5

"Data Modeling and Design

" emphasizes that a key goal of sound data modeling is to reduce data redundancy. It defines redundancy as the unnecessary repetition of data

which can be within a single record (as a derivable attribute) or across a database.

3. Stanford University. (2016). CS145: Introduction to Databases - Lecture 8: Functional Dependencies and Normalization. Retrieved from web.stanford.edu/class/cs145/slides/08-fds-and-normalization.pdf. Slide 30 discusses redundancy as information being "repeated unnecessarily" and illustrates the update

insertion

and deletion anomalies that arise from it

which is the core problem with having both a date of birth and a derivable birth month.

The UNION operator in SQL is used to combine the result sets of two or more SELECT statements into a single result set. It appends the rows from one table to another. For this operation to work, the tables must have the same number of columns, and the corresponding columns must have compatible data types. In this scenario, both the Managers and Non-managers tables share an identical structure, making UNION the correct method to create a single, consolidated report of all employees by stacking the rows from both tables vertically.

A. Group: This is used with aggregate functions (e.g., COUNT, SUM) to summarize data into groups, not to combine tables.

B. Join: This combines tables horizontally based on a related column, merging columns from multiple tables into a single row.

D. Nested: This refers to a subquery (a query within another query) and describes a query's structure, not the specific operation for combining these tables.

1. Microsoft SQL Server Documentation. (2023). UNION (Transact-SQL). Microsoft Docs. In the "Arguments" section

it is stated that UNION "Specifies that multiple result sets are to be combined and returned as a single result set." The basic example shows combining two tables with identical column structures.

2. PostgreSQL 16 Documentation. (2023). 7.4. Combining Queries. In this section

the documentation states

"The UNION operator computes the set union of the rows returned by the involved SELECT statements... The two SELECT statements that represent the direct operands of the UNION must produce the same number of columns

and corresponding columns must be of compatible data types."

3. Ullman

J. D.

& Widom

J. (n.d.). CS145 Introduction to Databases Course Notes: More SQL. Stanford University. In the section on "Union

Intersection

and Difference of Relations

" the notes specify that UNION is the standard set-union operator for SQL

requiring the tables to be "union-compatible

" which the tables in the question are.

The most effective way to find all customers who have not placed an order is to use a LEFT JOIN. This query starts with the Customers table (the "left" table) and joins it to the Products table. A LEFT JOIN returns all records from the Customers table, regardless of whether there is a matching record in the Products table. If a customer has no orders, the columns from the Products table for that customer's row will be populated with NULL. The WHERE P.CustomerID IS NULL clause then filters the results to show only those rows where no match was found, effectively isolating the customers without orders.

B. This query uses an INNER JOIN, which would only return customers who have placed an order. It also contains multiple syntax errors, such as an invalid join condition and the use of an aggregate function (COUNT) in a WHERE clause.

C. This query also incorrectly uses an INNER JOIN, which would filter out customers without orders from the start. The WHERE clause contains a syntactically invalid subquery that attempts to use a boolean expression.

D. This query performs a LEFT JOIN from Products to Customers and filters for non-null CustomerIDs. This would return all orders and their associated customer information, which is the opposite of the desired outcome.

---

1. Silberschatz

A.

Korth

H. F.

& Sudarshan

S. (2020). Database System Concepts (7th ed.). McGraw-Hill. In Chapter 4

"Intermediate SQL

" the section on "Outer-Join Expressions" describes how a LEFT OUTER JOIN preserves tuples from the left relation that do not have matching tuples in the right relation

extending them with null values. This is the foundational principle for the correct answer's logic.

2. PostgreSQL 16 Documentation. (2023). "7.2.2. JOIN Clauses". The documentation for LEFT OUTER JOIN states: "For each row R1 of T1

the joined table has a row for each row in T2 that satisfies the join condition with R1. ... If no join partner exists in T2

a row is generated with null values in the T2 columns." This directly supports the mechanism of using WHERE ... IS NULL to find non-matching rows.

3. SQLite Documentation. "The LEFT and CROSS JOIN keywords". The official documentation explains: "If the ON or USING clause of a LEFT JOIN is not satisfied for some row in the left-hand table

then a single row is output for that left-hand table row with NULL values in the columns that would have been filled from the right-hand table." This confirms the behavior exploited by the correct query.

A data lake is a centralized storage repository designed to hold vast amounts of raw data in its native format. The fundamental principle of a data lake is to ingest and store data "as-is" from various sources without prior transformation or structuring (a concept known as schema-on-read). This approach preserves the original, unaltered data, making it available for diverse and future analytical purposes. In contrast, other repositories like data warehouses require data to be cleaned, transformed, and structured into a predefined schema before it is loaded (schema-on-write), thereby altering it from its original state.

B. Data warehouse: Stores structured and transformed data that has been processed (ETL/ELT) to fit a predefined schema, meaning it is not in its original, unaltered form.

C. Data table: A specific structure within a database that holds organized, structured data, which by definition has been processed to fit the table's schema.

D. Data factory: A cloud service for orchestrating and automating data movement and transformation (ETL/ELT pipelines); it is a processing tool, not a storage repository.

1. Microsoft Azure Documentation. (2023). What is Azure Data Lake?. "A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is

without having to first structure the data..."

2. Amazon Web Services (AWS) Documentation. (n.d.). What is a Data Lake?. "A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as is

without having to first structure the data..."

3. Nargesian

F.

Ghafori

E.

Toosi

A.

& van der Aalst

W. M. P. (2019). Data Lake Governance: A Survey. ACM Computing Surveys

52(4)

Article 80

p. 2. "A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed." DOI: https://doi.org/10.1145/3342260

4. Hai

R.

Geisler

S.

& Quix

C. (2016). Constance: An Intelligent Data Lake System. In Proceedings of the 2016 International Conference on Management of Data (SIGMOD '16). Association for Computing Machinery

New York

NY

USA

2097–2100. Section 2

"Data Lakes". "Data lakes keep the data in its raw format and only apply a schema when the data is read (schema-on-read)." DOI: https://doi.org/10.1145/2882903.2903740