IT: D427 ITEC 2117 Data Management
Applications
Final Assessment Review
Questions & Solutions
2025
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, Question 1 (Multiple Choice)
Case Study:
A logistics company is developing a cloud‑based application to ingest and
analyze continuous sensor data from its fleet. The incoming data includes
both structured (e.g., GPS coordinates) and unstructured information
(e.g., images and log files). The solution must scale with data volume over
time.
Question:
Which data management solution is most appropriate for handling both
structured and unstructured data at scale?
a) Traditional Relational Database Management System (RDBMS)
b) Data Warehouse
c) Data Lake
d) In‑Memory Database
Correct ANS: c) Data Lake
Rationale:
Data lakes are designed to store vast amounts of raw data in its native
format—structured, semi‑structured, or unstructured—allowing
organizations to ingest sensor data without predefined schemas. This
flexibility and scalability make data lakes ideal for IoT and big data
applications.
---
Question 2 (Fill in the Blank)
Case Study:
An enterprise is modernizing its data integration processes. Instead of
physically consolidating data, the IT team plans to utilize a technique that
©2025
,allows applications to access multiple data sources in real time, without
unnecessary data movement.
Question:
The technique that provides real‑time access to disparate data sources
without physically moving the data is known as ______.
Correct ANS: data virtualization
Rationale:
Data virtualization creates an abstraction layer that enables applications
to query and integrate data from diverse sources as if it resided in a
single repository. This approach reduces data movement overhead and
improves real‑time access across heterogeneous systems.
---
Question 3 (True/False)
Case Study:
A retailer has implemented a SQL‑on‑Hadoop solution to enable analysts
to run queries on massive datasets stored in Hadoop while using familiar
SQL syntax.
Question:
True or False: SQL‑on‑Hadoop engines combine Hadoop’s scalability with
the familiarity of SQL, supporting real‑time analytical queries on big data.
Correct ANS: True
Rationale:
SQL‑on‑Hadoop solutions (such as Apache Hive, Impala, or Presto) allow
users to run SQL queries over data stored in Hadoop’s distributed file
system. They leverage Hadoop’s scalability while providing a
conventional query interface, making them well suited to big data
©2025
, analytics.
---
Question 4 (Multiple Response)
Case Study:
A financial services firm is deploying a master data management (MDM)
application to ensure consistent customer information across multiple
operational systems. The system must integrate data, cleanse duplicates,
and enforce governance.
Question:
Select all that apply: Which components are essential for an effective
MDM solution?
a) Data integration layer
b) Data cleansing and matching engine
c) Governance framework
d) Standalone spreadsheet management
Correct ANSs: a) Data integration layer, b) Data cleansing and
matching engine, c) Governance framework
Rationale:
An effective MDM solution includes components for integrating data
from various sources, cleansing and matching records to eliminate
duplicates, and establishing a governance framework to enforce data
quality standards. Standalone spreadsheets are not considered scalable
or robust for MDM.
---
Question 5 (Multiple Choice)
Case Study:
©2025
Applications
Final Assessment Review
Questions & Solutions
2025
©2025
, Question 1 (Multiple Choice)
Case Study:
A logistics company is developing a cloud‑based application to ingest and
analyze continuous sensor data from its fleet. The incoming data includes
both structured (e.g., GPS coordinates) and unstructured information
(e.g., images and log files). The solution must scale with data volume over
time.
Question:
Which data management solution is most appropriate for handling both
structured and unstructured data at scale?
a) Traditional Relational Database Management System (RDBMS)
b) Data Warehouse
c) Data Lake
d) In‑Memory Database
Correct ANS: c) Data Lake
Rationale:
Data lakes are designed to store vast amounts of raw data in its native
format—structured, semi‑structured, or unstructured—allowing
organizations to ingest sensor data without predefined schemas. This
flexibility and scalability make data lakes ideal for IoT and big data
applications.
---
Question 2 (Fill in the Blank)
Case Study:
An enterprise is modernizing its data integration processes. Instead of
physically consolidating data, the IT team plans to utilize a technique that
©2025
,allows applications to access multiple data sources in real time, without
unnecessary data movement.
Question:
The technique that provides real‑time access to disparate data sources
without physically moving the data is known as ______.
Correct ANS: data virtualization
Rationale:
Data virtualization creates an abstraction layer that enables applications
to query and integrate data from diverse sources as if it resided in a
single repository. This approach reduces data movement overhead and
improves real‑time access across heterogeneous systems.
---
Question 3 (True/False)
Case Study:
A retailer has implemented a SQL‑on‑Hadoop solution to enable analysts
to run queries on massive datasets stored in Hadoop while using familiar
SQL syntax.
Question:
True or False: SQL‑on‑Hadoop engines combine Hadoop’s scalability with
the familiarity of SQL, supporting real‑time analytical queries on big data.
Correct ANS: True
Rationale:
SQL‑on‑Hadoop solutions (such as Apache Hive, Impala, or Presto) allow
users to run SQL queries over data stored in Hadoop’s distributed file
system. They leverage Hadoop’s scalability while providing a
conventional query interface, making them well suited to big data
©2025
, analytics.
---
Question 4 (Multiple Response)
Case Study:
A financial services firm is deploying a master data management (MDM)
application to ensure consistent customer information across multiple
operational systems. The system must integrate data, cleanse duplicates,
and enforce governance.
Question:
Select all that apply: Which components are essential for an effective
MDM solution?
a) Data integration layer
b) Data cleansing and matching engine
c) Governance framework
d) Standalone spreadsheet management
Correct ANSs: a) Data integration layer, b) Data cleansing and
matching engine, c) Governance framework
Rationale:
An effective MDM solution includes components for integrating data
from various sources, cleansing and matching records to eliminate
duplicates, and establishing a governance framework to enforce data
quality standards. Standalone spreadsheets are not considered scalable
or robust for MDM.
---
Question 5 (Multiple Choice)
Case Study:
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