Data Warehousing and Business Intelligence Quiz

Data warehouses were seen as a means to?

Enable IT to take over business decisions related to data
Reduce the proliferation of decision support systems, most of which drew on the same core enterprise data
Improve analytics and reduce enterprise cost for the marketing department
Store all the organisation's metadata in a central repository
All of the above

Which of these is NOT an important activity in Data Warehousing and Business Intelligence?

Populate the Data Warehouse
Maintain the Data Warehouse and Business Intelligence architecture
Understand business requirements
Develop the Data Warehouse and Data Marts
Establish existing Data Architecture specifications and roadmap

When implementing a Data Warehouse, a principle to follow is building metadata along with the Data Warehouse. This is because:

A critical aspect to the success of the Data Warehouse is the capability to explain the data
Metadata management is the only priority of all Data Warehouse projects
Metadata is not captured as part of the development cycle
A Data Warehouse is comprised only of metadata
Metadata management tools have been purchased and need to be populated

Business Intelligence tools enable:

Dashboarding capabilities
Data mining
Data visualization
Advance analytics
All of the above

Bill Inmon and Ralph Kimball have different approaches to developing Data Warehouses. They both however have similar principles. Which of these is an area of difference between the two?

Data stored in a Data Warehouse is organised in a fashion that increases its value
Data Warehouses enable accessibility and usability of data for analysis
A key purpose of adopting a Data Warehouse is to allow stakeholders to find and trust their data
A Data Warehouse stores data from other systems
Data Warehouses store data in Star Schemas to enable query performance

What is NOT true of Data Warehouses, according to Bill Inmon?

Data Warehouses are an integrated system of record for data
A Data Warehouse is volatile as records are updated constantly
Data Warehouses focus on historical data
A Data Warehouse is organised based on business entities rather than applications
A Data Warehouse stores data like a snapshot, which shows the state of data at a point in time

According to Ralph Kimball, data in a Data Warehouse is stored in which model?

Dimensional data model
Conceptual data model
Logical data model
Enterprise data model
None of the above

In Kimball’s Data Warehouse Chess Pieces view of DW/BI architecture, which component most influences the quality of the analytics being delivered?

Analytical applications
Data staging area
Operational source systems
Data access tools
Data presentation area

What is the key difference in the data lifecycle of a DW/BI landscape when dealing with big data?

Data is ingested into the big data tool before integrating it
There are no differences in data lifecycles
Data is transformed twice. Prior to extraction from source system and again when loaded into a Data Warehouse
When extracting big data, they are automatically cleansed in the process
Big data solutions integrate data before landing it in tables

What is a data mart?

Another term for a Data Warehouse
A subset of a data warehouse that aggregates data but slows down analysis
A data store holding warehouse data for a specific subject area, a single department, or a single business process
A normalised data Data Warehouse
An extension of an Operational Data Store

What is NOT true of a Data Vault?

Data is cleansed and standardized as part of the staging process
History is stored in a normalized atomic structure, dimensional surrogate, primary and alternate keys are defined
Business key and surrogate key relationships should remain intact to provide the history of the data mart
Reloading facts is possible when later increments introduce grain changes
Auditing and tracing data is not part of the approach as there is no need to trace back to the data source

When gathering requirements for Data Warehouse projects, we should start with:

Identifying critical data elements
Cataloging your metadata
IT decides where to start based on technological requirements
Business goals and strategy
None of the above

There are typically 3 concurrent development tracks in Data Warehouse and Business Intelligence projects. What are they?

Data, Business Intelligence tools, Technology
Master data management, Infrastructure, Data Quality
Data modelling, Metadata, Architecture
Technology, Metadata, Data Quality
Data, Business Intelligence tools, Terminology

When performing source to target mapping, one of the main challenges with any mapping effort is:

Documenting the lineage for each data element back to its source
Determining valid links or equivalencies between data elements in multiple systems
Cleaning or transforming the data beforehand
Deleting fields that contain duplicate data
Identifying the relevant data steward to perform the mapping

When defining a population approach for the Data Warehouse, we should consider:

Dimensional aspects
Target databases
Required latency
Batch windows or upload intervals
All of the above

When defining target user group for BI tools, we should:

Understand the BI needs of the user groups and then complement the right tool to the user groups
Roll out the most comprehensive BI tool to ensure all users have the best tools
Let the user groups willing to pay the most determine the groups
Train all user groups on the selected BI tool so they can function as one holistic group
Focus on the needs of the most advanced users only

Which process is critical to the incremental development, production, deployment and regular maintenance of a Data Warehouse?

Content Management
Application lifecycle management
Continuous deployment
Release Management
Data Management Lifecycle

What are ways we can monitor and adjust Business Intelligence performance and activity?

Display and define customer facing satisfaction metrics such as query response times and daily active users
Survey the users of the Data warehouse or BI tools on a regular basis
Review usage statistics to reveal bottlenecks and apply optimisation efforts
Provide a dashboard with high-level status of data delivery activities and drill down capabilities
All of the above

When selecting a DW/BI tool for your organisation, which is NOT a factor to consider?

Leverage decision criteria tools, process implementation tools and professional services to facilitate the selection process
IT preferences based on their experience with best in class tools
Evaluate between buying of the shelf, building your own tool in house or SaaS options
Define your near term requirements, potential future requirements and nonfunctional specifications
Upgrade, maintenance and replacement costs

Data lineage analysis is useful for the following functions EXCEPT:

Investigation of the root causes of data issues
Rectifying and cleansing data issues on the spot
Impact analysis for system changes or data issues
Determining the reliability of data, based on its origin
Understanding how data transforms through systems

What implementation approaches do not support OLAP?

Relational Online Analytical Processing
Hybrid Online Analytical Processing
Flat Online Analytics Processing
Multi-dimensional Online Analytical Processing
All of the above

Why should you profile data before starting a DW implementation?

Profiling the data reduces risk associated with unexpected data
Profiling discloses differences between sources that may present obstacles to data integration
Poor data quality practices that impact the source systems, such as inconsistent data entry often first manifests as a problem in the Data Warehouse
Evaluation of the state of the source data leads to more accurate up-front estimates for feasibility and scope of effort
All of the above

Which of these points do NOT describe Self service BI?

Allowing a user to interact with analytic reports and develop their own ad hoc reporting
Providing a myriad of reporting capabilities for users to create their own reports
Transferring the burden of IT answering queries from the business users to the Data Governance team
Enable reports created to be pushed into a portal where other users can access and retrieve information at their own time
Promote collaboration and content sharing across organisational boundaries

Data Warehouses can usually be implemented with which method?

Waterfall
Incremental
Agile
Iterative
All of the above

What is NOT a critical success factor when it comes to DW/BI projects?

Business goals and scope
Business sponsorship
IT requirements for more advanced tools
Business readiness
Vision alignment

When trying to meet requirements from the business during any DW/BI implementation, which of these architectural sub components should also be considered?

Conceptual Data Model
Data quality feedback loop
End-to-end Metadata
End-to-end verifiable data lineage
All of the above

Which of these is LEAST IMPORTANT when creating a reporting strategy for the BI Portfolio?

The type of user community and the right tool for those consumers
The statutory reporting needs of the regulatory agencies
Access mechanisms to describe how users want to interact, report, examine or view their data
Trade-offs between performance and timeliness
Security access that restricts unapproved users from accessing sensitive data

Establishing usage metrics typically involves tracking all the licensed account users and their activity. A good way to measure this is:

Getting users to self report how much they use a certain tool
Training the users to leverage the tools you have purchased for them
Senior management should dictate usage activity and everyone must follow so there is no real need to track usage activity
Tracking how long a user stays logged in to the tool
Monitoring how many users actually connect to the tool and how many queries are sent in a given duration of time

What is one of the major differences between operational systems and data warehouses?

Operational systems are online all the time; data warehouses are available only at month end
Operational systems focus on data retention; data warehouses focus on data security
Operational systems process batch data; data warehouses support business analytics
Operational systems focus on historical data; data warehouses contain current data
Operational systems focus on current data; data warehouses contain historical data

Which of these is a core idea of Data Warehousing shared by both Inmon and Kimball?

Storing and organising data in a Data Warehouse increases its value
Storing and organising data in a Data Warehouse increases its cost
Storing and organising data in a Data Warehouse increases its risk profile
Storing and organising data in a Data Warehouse helps us back up archived data
Storing and organising data in a Data Warehouse decreases its cost

When supporting multi-dimensional business report requests, which would be best?

OLAP
ODS
BI
OLTP
CDC

Which of these reporting types would be least suited to BI?

Management dashboards
Fraud detection and reporting
Decision support systems
Statutory reporting to a Regulatory Body
Sales forecasting and demand planning

What kind of data processing uses Slice, Dice, Roll-up and Pivot?

FTP
OLTP
CDC
ETL
OLAP

When estimating the level of effort needed to implement different DW technologies, which is least important?

How much of the tool infrastructure meets our organisational infrastructure
The Standard source systems for which ETL is supplied
Annual costs such as license, maintenance, etc.
Number of source systems we need to integrate into the tool
How much do the canned processes in the tool match our business

Data Warehouses need to be incrementally developed. Which process is critical to the success of such a project?

A strong release management process
A strong incident management process
An agile development process
A strong change management process
A strong capacity assessment process

What would be the broadest description of the contents of a Data Warehouse?

Data stores and extracts that can be transformed into star schemas
Using an Inmon or Kimball approach
Any data stores or extracts used to support the delivery for BI purposes
Any data we have in the firm
The best place to ensure our data is clean and fit for purpose

Which of the following is most likely to be utilised by executives for assessing metrics aligned with long-term organisational goals?

Operational Analytics
Tactical Business Intelligence
Business Performance Management Reporting
Descriptive Analytics
Data Warehousing

Which of the following best describes a comprehensive approach to selecting data warehouse tools?

Evaluating build, buy, and rent options while considering near-term and future requirements
Focusing solely on immediate requirements
Prioritising the cheapest options available
Choosing tools based solely on vendor reputation
Opting for tools that require minimal setup time

Which step in the DW/BI implementation effort requires the most effort?

Vendor selection and due dilligence
Building the business case to invest in the project
Training and coaching the business to use the dashboards and reports
Preparing and processing the data
Establishing a standard release plan

Why is determining valid links or equivalencies between data elements when integrating multiple source systems such a challenge?

Because of the need for data quality feedback loops
Because of the complexity of end-to-end metadata
Because of the need for end-to-end verifiable data lineage
Because of the focus on data visualisation capabilities
Because each source system may have different data quality standards, complicating integration

In order to demonstrate progress towards the desired end state of your DW/BI program, which of these is most important?

Business Sponsorship
Release Roadmap
Data Quality Feedback Loop
End-to-end Metadata
Customer/User Satisfaction

Data Governance is critical to the success of Data Warehousing and BI projects. Which of these methods is least useful for getting Business Acceptance?

Providing the conceptual data model
Ensuring there's a Data Quality feedback loop
Focusing on the visual aesthetics of the BI tool interface
Delivering metadata from end-to-end
Delivering data lineage from source to target

End-to-end metadata is most useful for answering which of the following questions?

How can we improve data quality?
What does this report mean?
How can we optimize data storage
What are the best predictive models for our data?
How can we enhance data security?

What is the primary advantage of using OLAP cubes in financial analysis?

They allow for real-time data updates
They facilitate data normalization
They enable traversal through known hierarchies for data analysis
They eliminate the need for data cleansing
They provide a mechanism for data encryption

What metric is most useful for tuning BI activity and performance in a data warehouse/BI project?

Number of OLAP slices
Number of prototypes created and tested
Number of indexes used in the fact table
Number of users per day, week, or month
Number of data integration tools used

What source primarily informs the content of a data dictionary in a data warehouse?

Logical data model
Security restrictions
Physical data model
Developed population code
Data lineage analysis

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