The question asks for the next logical step after the technical requirements for a reporting deck have been met. In a standard data visualization or business intelligence project lifecycle, after the initial development is complete, the solution must be validated with stakeholders. Showing a mock-up or a functional prototype to the team serves as a crucial review and user acceptance testing (UAT) step. This process ensures the visualizations are clear, the metrics are correct, and the security functions as intended before the final analysis is formally presented or the report is deployed to a wider audience. This iterative feedback loop is essential for project success.

B. Explain the desired level of reporting detail.

This is a requirements-gathering activity that must be completed before the analyst begins building the report.

C. Present an analysis of the data.

This is the final step of the project. It should only occur after the reporting deck has been reviewed and approved by the team or key stakeholders.

D. Find out the project due date.

This is a fundamental project management task that is established during the initial planning phase, long before the development work is completed.

---

1. Wirth

R.

& Hipp

J. (2000). CRISP-DM: Towards a standard process model for data mining. Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining. Page 35

Section 3.5

"Evaluation". The CRISP-DM methodology

a standard in data projects

specifies an "Evaluation" phase that occurs after model/solution building but before final deployment. This phase involves reviewing the work against business objectives

which aligns with showing a prototype to the team for feedback.

2. Microsoft Corporation. (2023). Power BI adoption roadmap: BI solution planning. Microsoft Learn documentation. In the section "Project planning

" the documentation outlines the typical phases of a BI project

which include prototyping

development

and validation. The validation phase

which includes User Acceptance Testing (UAT)

occurs after development is complete and is essential before deployment. Showing a functional version to the team is a core part of this validation.

3. Munzner

T. (2014). Visualization Analysis and Design. CRC Press. Chapter 3

"A Nested Model for Visualization Design and Validation

" outlines a structured process for creating visualizations. The model emphasizes validation at each stage. After the "Algorithm Design" stage (equivalent to building the report)

the process requires validation to ensure the implementation is correct and effective before it is deployed for use. This academic model supports the practice of internal review and feedback post-development.

A data warehouse is the most appropriate choice. It is a centralized repository designed to integrate structured data from various sources across an entire enterprise. Its core architecture is built on a predefined schema (a "schema-on-write" approach), where data is cleaned, transformed, and structured before being loaded. This ensures data consistency and quality, making it ideal for company-wide analytics and business intelligence, which aligns perfectly with the scenario's requirements.

A. Data mart: This is a subset of a data warehouse, focused on a single business line or department, not the entire company.

C. Data silo: This refers to an isolated data repository, inaccessible to the rest of the organization, which is the opposite of an integrated, company-wide solution.

D. Data lake: A data lake stores raw data in its native format, including unstructured and semi-structured data, using a "schema-on-read" approach, which contradicts the requirement for a predefined data structure.

---

1. Microsoft Azure Documentation. (n.d.). What is a Data Warehouse?. Microsoft. Retrieved from https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-a-data-warehouse/.

Reference Detail: In the main definition

it states

"A data warehouse is a centralized repository of integrated data from one or more disparate sources. Data warehouses store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise." This supports the "whole company" and "structured data" aspects.

2. Amazon Web Services (AWS) Documentation. (n.d.). What is a Data Lake?. AWS. Retrieved from https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/.

Reference Detail: The section "Data Lake vs. Data Warehouse" explicitly contrasts the two: "A data warehouse is a database optimized to analyze relational data... The data structure and schema are defined in advance... A data lake is different

because it stores relational data... and non-relational data... without the need to have a predefined schema." This clarifies why a data lake is incorrect.

3. Sawadogo

P.

& Darmont

J. (2021). Data Lakes: A Survey. The VLDB Journal

30(1)

137-166. https://doi.org/10.1007/s00778-020-00633-5

Reference Detail: Section 2.1

"Data Warehouses

" states: "Data warehouses (DWs) are mature decision-support systems that provide high-quality

cleansed

and consolidated data... DWs are based on a schema-on-write approach

where data must conform to a predefined schema before being loaded." This peer-reviewed source confirms the "predefined data structure" requirement for data warehouses.

4. Chaudhuri

S.

& Dayal

U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record

26(1)

65-74. https://doi.org/10.1145/248603.248616

Reference Detail: Section 2

"What is a Data Warehouse?"

defines it as a "collection of decision support technologies

aimed at enabling the knowledge worker... to make better and faster decisions." It emphasizes the integration of data from multiple

heterogeneous sources into a single

structured repository.

The task is to "flag" customers based on a specific criterion: whether the Month is 'January'. This requires an operation that evaluates a condition for each row and returns a result based on whether that condition is true or false. Logical functions, such as IF or CASE, are designed specifically for this purpose. They test a condition (e.g., Month = 'January') and return a corresponding value (e.g., 1 for true, 0 for false), effectively creating a flag.

A. Statistical: Statistical functions are used to perform aggregations on a set of data, such as calculating SUM, AVERAGE, or COUNT, not for evaluating a row-by-row condition.

C. Mathematical: Mathematical functions perform arithmetic calculations like addition or multiplication on numeric data. They are not used for evaluating conditional statements on text values.

D. Date: Date functions are used to manipulate or extract components from date/time data types. The operation here is a logical comparison of a text string, not a date manipulation.

---

1. Microsoft Corporation. (n.d.). Logical functions in Data Analysis Expressions (DAX). Microsoft Learn. Retrieved from the official Microsoft documentation. The documentation for the IF function states

"The IF function checks a condition... and returns one value if the condition is TRUE

otherwise it returns a second value." This directly describes the process of flagging based on a condition.

2. The PostgreSQL Global Development Group. (2023). Chapter 9. Functions and Operators

Section 9.18. Conditional Expressions. PostgreSQL 16 Documentation. This official documentation describes the CASE expression

which "provide[s] a general conditional expression

similar to if/else statements." A common use is CASE WHEN month = 'January' THEN 'Yes' ELSE 'No' END

which is a direct implementation of the flagging logic required in the question.

3. VanderPlas

J. (2016). Python Data Science Handbook. O'Reilly Media. Chapter 3.02

"Data Indexing and Selection". In the context of data analysis with Python's Pandas library

this operation is achieved through boolean masking (e.g.

df['Month'] == 'January'). The handbook describes this as a logical operation that produces a boolean series

which can be used to flag

select

or filter data. This is a foundational concept taught in data science curricula.

Containerization is a method of operating-system-level virtualization used to deploy and run applications in isolated spaces called containers. A container packages an application's code along with all its necessary dependencies, such as libraries, system tools, and runtime environments. This creates a single, lightweight, executable software package that is portable and can run consistently and reliably across various computing environments, from a local machine to a cloud infrastructure.

A. Package manager: A package manager installs and manages software packages and their dependencies for a specific operating system or programming language, not for cross-environment portability.

B. Code repository: A code repository is a storage location for managing and tracking changes to source code (e.g., Git); it does not package the application for execution.

C. Virtual machine: A virtual machine emulates an entire computer system, including a full guest operating system, making it much more resource-intensive than just packaging an application and its libraries.

1. Docker Inc. (Official Vendor Documentation). "What is a Container?". Docker Resources. Accessed October 2023. The documentation states

"A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another."

2. Bernstein

D. (2014). "Containers and Cloud: From LXC to Docker to Kubernetes." IEEE Cloud Computing

1(3)

pp. 81-84. DOI: 10.1109/MCC.2014.51. This paper (Section: "What Is a Container?") describes containers as a technology that packages an application with its runtime dependencies

enabling it to run on any infrastructure.

3. University of California

Berkeley. "CS 162: Operating Systems and Systems Programming

" Lecture 22: Virtual Machines. The course materials often contrast VMs with containers

defining containers as a lightweight alternative that bundles the application and its dependencies to run on a shared host OS kernel

ensuring environmental consistency.

This SQL query correctly addresses the user's request. The SELECT statement retrieves the cellular number, the carrier, and the sum of call durations. The SUM(callduration) is an aggregate function that calculates the total duration. When using an aggregate function, all other columns in the SELECT list that are not part of an aggregate function must be included in the GROUP BY clause. This query correctly groups the results by both cellularnumber and callednumbercarrier. Finally, the ORDER BY callednumbercarrier clause sorts the output as required.

A. The GROUP BY clause is incomplete. It must include all non-aggregated columns from the SELECT list, but it omits callednumbercarrier.

B. The GROUP BY clause incorrectly groups by callduration instead of the required cellularnumber, and it attempts to sort by a column not in the final output.

D. The GROUP BY clause is incorrect. It groups by callduration and omits the non-aggregated columns cellularnumber and callednumbercarrier from the SELECT list.

1. PostgreSQL 16 Documentation

Section 7.2.4. Grouping and Window Functions: "When GROUP BY is present

or if any aggregate functions are present

it is not valid for the SELECT list expressions to refer to ungrouped columns except within aggregate functions or when the ungrouped column is functionally dependent on the grouped columns

since there would otherwise be more than one possible value to return for an ungrouped column." This rule directly invalidates options A

B

and D

while confirming C is correct.

2. Stanford University

CS 145

Introduction to Databases

Lecture Notes on SQL Aggregation: The notes state the fundamental rule of SQL aggregation: "Attributes in SELECT list that are not aggregated must be in GROUP BY list." This principle is the basis for why option C is the only valid query among the choices.

3. MIT OpenCourseWare

6.830/6.814: Database Systems

Fall 2018

Lecture 2 Notes

Section 3.2 Aggregation: "The GROUP BY clause partitions the set of tuples into groups. The aggregate is then applied to each group... Any attribute that is not aggregated in the SELECT clause must appear in the GROUP BY clause." This academic source reinforces the standard SQL syntax and logic used in the correct answer.

The core issue is data staleness; the report is not reflecting recent updates from its source. The most direct and common cause for this is a failure in the data refresh process. This process is fundamentally dependent on the report's connection to the source database. The analyst should first verify the status of the database connection, including its configuration, credentials, and the success of the last scheduled data refresh. A failed, misconfigured, or unscheduled refresh is the most probable reason the new data is not appearing in the report.

A. Event log: An event log is a secondary diagnostic tool. It is more efficient to first check the component directly responsible for data updates (the connection/refresh) before searching through logs.

B. User privileges: The user can view the report, which means access privileges are not the problem. The issue is with the timeliness of the data itself, not the user's ability to access it.

D. Report corruption: Corruption would typically manifest as loading failures, visual errors, or an inability to open the report, not as the display of perfectly structured but outdated information.

1. Microsoft Power BI Documentation: In troubleshooting data refresh scenarios

the primary steps involve checking the data source connection. "A common reason for refresh failures is that Power BI can't sign in to your data sources... Check your data source credentials in the dataset settings." This confirms that the connection is the first point of inspection for stale data.

Source: Microsoft Learn

"Troubleshoot refresh scenarios

" Section: "Refresh fails because of sign-in problems."

2. Lemahieu

W.

vanden Broucke

S.

& Baesens

B. (2018). Principles of Data Management: Facilitating Information Sharing. Cambridge University Press. The text describes the Business Intelligence (BI) architecture where reports query a data warehouse or database. It states

"The data loading step is typically executed on a periodic basis (e.g.

nightly) to provide the business users with recent information." A failure in this loading or refresh process

which depends on the connection

is a primary cause of outdated reports.

Reference: Chapter 10

"Business Intelligence

" Section 10.2.1

"Data Warehouse Architecture."

3. Kimball

R.

& Ross

M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley. The book emphasizes the importance of the Extract

Transform

and Load (ETL) system for populating the data warehouse that BI tools query. A failure in any part of this pipeline

starting with the connection to the source (Extract)

will result in stale data for end-users. Verifying the data pipeline's operational status is a primary troubleshooting step.

Reference: Chapter 2

"The Data Warehouse/Business Intelligence Architecture

" Section on "The ETL System."

Foundational models, particularly those built on the transformer architecture, represent a state-of-the-art approach for time-series forecasting. These large models are pre-trained on vast amounts of sequential data, enabling them to learn complex patterns, seasonality, and long-range dependencies. They can then be fine-tuned for specific forecasting tasks, often achieving superior accuracy compared to traditional statistical methods or other neural network types. Their ability to handle long input sequences makes them exceptionally well-suited for complex time-series problems.

A. Generative AI: This category's primary purpose is to create new content (e.g., text, images), not to perform predictive analysis on historical numerical data for forecasting.

C. Natural language processing: This field of AI is specifically concerned with understanding and processing human language, not the numerical, time-ordered data characteristic of time-series.

D. Robotic process automation: This technology automates rule-based business processes and digital tasks; it lacks the analytical and predictive capabilities required for forecasting.

---

1. Gruver

N.

et al. (2024). "Large Language Models are Zero-Shot Time Series Forecasters." Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Section 1

Paragraph 2 discusses the limitations of existing methods and the potential for large

pre-trained models (i.e.

foundational models) to be adapted for time-series forecasting tasks.

2. Zhou

T.

et al. (2023). "One Fits All: A Generative Foundation Model for Time Series Analysis." Proceedings of the 40th International Conference on Machine Learning

PMLR 202

pp. 41933-41951. The abstract and introduction explicitly propose a "foundation model for time series analysis" to handle diverse datasets and tasks

including forecasting.

3. Rasul

K.

et al. (2024). "Chronos: Learning the Language of Time Series." Amazon Science. This official publication introduces Chronos

a family of pre-trained foundational models specifically designed for time-series forecasting

demonstrating a direct industry application of this concept. Available at: https://www.amazon.science/publications/chronos-learning-the-language-of-time-series

4. Vaswani

A.

et al. (2017). "Attention is All You Need." Advances in Neural Information Processing Systems 30 (NIPS 2017). This paper introduced the Transformer architecture

which is the core technology behind most modern foundational models and has been successfully adapted from its original NLP context to time-series forecasting.

A user information database contains Personally Identifiable Information (PII), which is data that can be used to identify a specific individual. Due to its potential for misuse and legal protection requirements (e.g., GDPR, CCPA), PII is classified as both Sensitive and Private. Sensitive data requires a high level of protection against unauthorized disclosure, and Private data is information about individuals that is not public. Implementing security triggers is a technical control used to audit access and prevent unauthorized actions on such high-risk data, which is consistent with these classifications.

A. Public: Public data has no confidentiality requirements and would not necessitate special security triggers for protection.

B. Open: Open data is intended for unrestricted access and sharing, which is the opposite of protected user information.

D. Non-Sensitive: This classification is for data with low impact if disclosed and would not warrant the implementation of security triggers.

F. Encrypted: Encryption is a security control or a data state, not a data classification level. Data is classified first, then controls like encryption are applied.

1. National Institute of Standards and Technology (NIST). (2010). Guide to Protecting the Confidentiality of Personally Identifiable Information (PII) (Special Publication 800-122). Section 2.2

"PII Confidentiality Impact Levels

" discusses how PII is categorized based on sensitivity and the potential impact of a breach

directly linking user information to the "Sensitive" concept.

2. Carnegie Mellon University

Information Security Office. Guidelines for Data Classification. This university guideline defines "Private" data as including names

email addresses

and other PII. It also defines "Sensitive" data as a category requiring a high level of security

which includes private data. This establishes the use of both terms for user information.

3. Oracle Corporation. Oracle Database Security Guide

21c. Chapter 1

"Introduction to Oracle Database Security

" discusses the importance of identifying sensitive data within the database to apply appropriate security controls. This aligns with the practice of classifying user information as sensitive to justify implementing measures like security triggers.

User Acceptance Testing (UAT) is the final phase in the software testing process. It is performed by the end-users or client representatives to validate that the software system meets the agreed-upon business requirements and is fit for its intended purpose from a user's perspective. The primary goal of UAT is to gain confidence and formal acceptance from the user that the system is ready for operational use. This involves testing the software in a real-world or simulated operational environment to ensure it can handle the required tasks and workflows.

A. UAT is a final validation stage that occurs before deployment, not at the beginning of the development process.

B. Ensuring different parts of an application work together is the purpose of Integration Testing, not UAT.

C. Reviewing crashes and deploying patches is part of the post-deployment maintenance and support phase, which happens after UAT.

1. ISO/IEC/IEEE 29119-1:2013

Software and systems engineering — Software testing — Part 1: Concepts and definitions. Section 4.2

"Test levels

" defines acceptance testing as a test level focused on determining whether to accept the system

often performed by customers or users based on their requirements.

2. Bourque

P.

& Fairley

R. E. (Eds.). (2014). Guide to the Software Engineering Body of Knowledge (SWEBOK)

Version 3.0. IEEE Computer Society. In Chapter 4

"Software Testing

" Section 2.1.3

"Acceptance Testing

" it is stated that this testing determines if a system satisfies its acceptance criteria

enabling the customer to determine whether to accept the system.

3. University of Toronto

Department of Computer Science. (2021). CSC301: Introduction to Software Engineering - Lecture 10: Software Testing. The course material describes User Acceptance Testing (UAT) as the process where clients/users test to confirm the system meets their needs

serving as a final verification of required business functions.

In standard SQL, NULL is a special marker indicating that a data value does not exist. It is not equivalent to zero or an empty string. Standard comparison operators like = cannot be used to test for NULL because the result of any arithmetic comparison with NULL is also NULL (or unknown), which evaluates to false in a WHERE clause. The correct, standard-compliant operator to specifically test for the absence of a value is IS NULL. This syntax correctly identifies and filters for all records where the specified column is empty.

A. WHERE [ColumnName] = NULL is incorrect because the = operator cannot be used to test for NULL values; this expression will not return any rows.

C. WHERE [ColumnName] IS NOT NULL is the opposite of the required logic; it returns records where the field has a value, not where it is empty.

D. WHERE [ColumnName] = 'NULL' searches for the literal text string 'NULL', not for fields that are actually empty (i.e., have the NULL state).

1. PostgreSQL 16 Documentation. (2023). 9.2. Comparison Functions and Operators. In the section detailing comparison operators

it states

"Do not write expression = NULL because NULL is not 'equal to' NULL... To test for nullness

use the constructs IS NULL and IS NOT NULL." Retrieved from postgresql.org/docs/16/functions-comparison.html.

2. Microsoft Transact-SQL (T-SQL) Documentation. (2023). IS NULL (Transact-SQL). Microsoft Learn. This official documentation specifies

"To determine whether an expression is NULL

use IS NULL or IS NOT NULL instead of comparison operators (such as = or !=)." Retrieved from learn.microsoft.com/en-us/sql/t-sql/queries/is-null-transact-sql.

3. Ullman

J. D.

& Widom

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

Lecture 4: SQL 1. Stanford University. In the lecture slides covering SQL basics

slide 28 ("A Subtlety About NULLs") explicitly states: "Test for NULL using IS NULL and IS NOT NULL

not = or ." Retrieved from web.stanford.edu/class/cs145/slides/04-sql1.pdf.