CDW110 ACTUAL EXAM 2026 200
QUESTIONS WITH DETAILED CORRECT
ANSWERS GRADED A+
⩥ Chapter 1. (Study Checklist) Caboodle Console. Answer: The
Caboodle Console is a web application housed on the Caboodle server. It
includes the following:
Dictionary
Dictionary Editor
Executions
Work Queue
Configuration
⩥ Chapter 1. (Study Checklist) Data Warehouse. Answer: In a data
warehouse, multiple sources may load data pertaining to a single entity.
This means that more than one package may populate a given row in a
Caboodle table. As a result, there may be multiple business key values
associated with a single entity in a Caboodle table.
⩥ Chapter 1. (Study Checklist) ETL. Answer: Extract, Transform, Load
⩥ Chapter 1. (Study Checklist) SSIS Package. Answer: The architecture
of Caboodle includes a staging database and a reporting database. Data
is extracted from source systems (like Clarity), transformed in the
,staging database, and presented for users in the reporting database. This
movement of data is realized via a set of SQL Server Integration
Services (SSIS) packages.
⩥ Chapter 1. (Study Checklist) Data Lineage. Answer: Generally, data
lineage refers to the process of identifying the source of a specific piece
of information. In Caboodle, data lineage is defined at the package level.
⩥ Chapter 1. (Study Checklist) Star Schema. Answer: The standard
schema for a dimensional data model. The name refers to the image of a
fact table surrounded by many linked dimension tables, which loosely
resembles a star.
The Caboodle data model structure is based on a "star schema" ‐ where
one central fact table will join to many associated lookup or dimension
tables. This structure provides the foundation of the Caboodle data
model.
⩥ Chapter 1. (Study Checklist) DMC. Answer: DATA MODEL
COMPONENT
No table in Caboodle "stands alone." Each is considered part of a Data
Model Component, which refers to the collection of metadata tables that
support the ETL process and reporting views stored in the FullAccess
schema.
Each DMC gets a type. Strict table naming conventions are followed in
Caboodle, so that a table's suffix provides information about its structure
and purpose.
,These suffixes are:
· Dim for dimensions (e.g. PatientDim)
· Fact for facts (e.g. EncounterFact)
· Bridge for bridges (e.g. DiagnosisBridge)
· DataMart for data marts (e.g. HospitalReadmissionDataMart)
· AttributeValueDim for EAV tables (e.g. PatientAttributeValueDim)
· X for custom tables (e.g. CustomFactX)
⩥ Chapter 1. (Study Checklist) Staging Database. Answer: The
Caboodle database into which records are loaded by SSIS packages and
stored procedures.
⩥ Chapter 1. (Study Checklist) Reporting Database. Answer: The
architecture of Caboodle includes a staging database and a reporting
database. Data is extracted from source systems (like Clarity),
transformed in the staging database, and presented for users in the
reporting database. This movement of data is realized via a set of SQL
Server Integration Services (SSIS)
packages.
⩥ Chapter 1. (Study Checklist) Dbo Schema. Answer: STAGING
DATABASE
Import tables and Mapping tables live here. This is
primarily used by administrators for moving data into Caboodle.
, REPORTING DATABASE
The dbo schema stores reporting data and acts as the
data source for SlicerDicer. The Caboodle Dictionary reflects the
contents of the dbo schema.
⩥ Chapter 1. (Study Checklist) FullAccess Schema. Answer: STAGING
DATABASE
The FullAccess schema does not exist on the Staging database.
REPORTING DATABASE
The FullAccess schema houses views that simplify reporting. FullAccess
should be your default schema when reporting.
⩥ (ETL Terms) Execution. Answer: An execution is the process that
extracts data from a source system using packages, transforms the data
in the staging database, and loads it to Caboodle for reporting. You
create and run executions in the Caboodle Console.
⩥ (ETL Terms) Extract. Answer: Extracts to Caboodle from Clarity can
be either backfill or incremental. Backfill extracts load or reload every
row in a table from Clarity, whereas incremental extracts load only
changed rows. Existing data is available while extracts are in progress.
QUESTIONS WITH DETAILED CORRECT
ANSWERS GRADED A+
⩥ Chapter 1. (Study Checklist) Caboodle Console. Answer: The
Caboodle Console is a web application housed on the Caboodle server. It
includes the following:
Dictionary
Dictionary Editor
Executions
Work Queue
Configuration
⩥ Chapter 1. (Study Checklist) Data Warehouse. Answer: In a data
warehouse, multiple sources may load data pertaining to a single entity.
This means that more than one package may populate a given row in a
Caboodle table. As a result, there may be multiple business key values
associated with a single entity in a Caboodle table.
⩥ Chapter 1. (Study Checklist) ETL. Answer: Extract, Transform, Load
⩥ Chapter 1. (Study Checklist) SSIS Package. Answer: The architecture
of Caboodle includes a staging database and a reporting database. Data
is extracted from source systems (like Clarity), transformed in the
,staging database, and presented for users in the reporting database. This
movement of data is realized via a set of SQL Server Integration
Services (SSIS) packages.
⩥ Chapter 1. (Study Checklist) Data Lineage. Answer: Generally, data
lineage refers to the process of identifying the source of a specific piece
of information. In Caboodle, data lineage is defined at the package level.
⩥ Chapter 1. (Study Checklist) Star Schema. Answer: The standard
schema for a dimensional data model. The name refers to the image of a
fact table surrounded by many linked dimension tables, which loosely
resembles a star.
The Caboodle data model structure is based on a "star schema" ‐ where
one central fact table will join to many associated lookup or dimension
tables. This structure provides the foundation of the Caboodle data
model.
⩥ Chapter 1. (Study Checklist) DMC. Answer: DATA MODEL
COMPONENT
No table in Caboodle "stands alone." Each is considered part of a Data
Model Component, which refers to the collection of metadata tables that
support the ETL process and reporting views stored in the FullAccess
schema.
Each DMC gets a type. Strict table naming conventions are followed in
Caboodle, so that a table's suffix provides information about its structure
and purpose.
,These suffixes are:
· Dim for dimensions (e.g. PatientDim)
· Fact for facts (e.g. EncounterFact)
· Bridge for bridges (e.g. DiagnosisBridge)
· DataMart for data marts (e.g. HospitalReadmissionDataMart)
· AttributeValueDim for EAV tables (e.g. PatientAttributeValueDim)
· X for custom tables (e.g. CustomFactX)
⩥ Chapter 1. (Study Checklist) Staging Database. Answer: The
Caboodle database into which records are loaded by SSIS packages and
stored procedures.
⩥ Chapter 1. (Study Checklist) Reporting Database. Answer: The
architecture of Caboodle includes a staging database and a reporting
database. Data is extracted from source systems (like Clarity),
transformed in the staging database, and presented for users in the
reporting database. This movement of data is realized via a set of SQL
Server Integration Services (SSIS)
packages.
⩥ Chapter 1. (Study Checklist) Dbo Schema. Answer: STAGING
DATABASE
Import tables and Mapping tables live here. This is
primarily used by administrators for moving data into Caboodle.
, REPORTING DATABASE
The dbo schema stores reporting data and acts as the
data source for SlicerDicer. The Caboodle Dictionary reflects the
contents of the dbo schema.
⩥ Chapter 1. (Study Checklist) FullAccess Schema. Answer: STAGING
DATABASE
The FullAccess schema does not exist on the Staging database.
REPORTING DATABASE
The FullAccess schema houses views that simplify reporting. FullAccess
should be your default schema when reporting.
⩥ (ETL Terms) Execution. Answer: An execution is the process that
extracts data from a source system using packages, transforms the data
in the staging database, and loads it to Caboodle for reporting. You
create and run executions in the Caboodle Console.
⩥ (ETL Terms) Extract. Answer: Extracts to Caboodle from Clarity can
be either backfill or incremental. Backfill extracts load or reload every
row in a table from Clarity, whereas incremental extracts load only
changed rows. Existing data is available while extracts are in progress.
