D427 ITEC 2117
Data Management Applications
4.0 Credits
Objective Assessment Review (Qns &
Ans)
2025
©2025
, Multiple Choice Questions
Question 1:
A multinational analytics firm is implementing a real‑time dashboard to monitor IoT
sensor data. Which advanced data management application is best suited to
process continuous data streams in real time?
A. Batch processing with Hadoop MapReduce
B. Stream processing platforms (e.g., Apache Kafka and Spark Streaming)
C. Traditional relational database systems
D. OLAP cubes
Correct ANS: B. Stream processing platforms (e.g., Apache Kafka and Spark
Streaming)
Rationale:
Stream processing platforms are designed to handle continuous, real‑time data
flows. They ingest and process data as it arrives, which is critical for real‑time
dashboards that monitor IoT sensors, unlike batch processing or traditional
databases that introduce latency.
---
Question 2:
An enterprise needs a scalable solution to ingest and store vast amounts of
unstructured and semi‑structured data from diverse sources. Which application
best addresses these requirements?
A. Traditional Data Warehouse
B. Data Lake
C. OLTP Database
D. In‑memory Database
Correct ANS: B. Data Lake
Rationale:
Data lakes are optimized for massive storage and ingestion of diverse data types
with schema‑on‑read capabilities, making them ideal for unstructured and
semi‑structured data. Traditional data warehouses require rigid schemas and are
less flexible for diverse data sets.
©2025
, ---
Question 3:
Which of the following best describes the schema‑on‑read approach commonly
implemented in modern data management applications?
A. Defining data schema prior to storage
B. Applying schema when data is queried
C. Enforcing a fixed schema across all data sources
D. Using OLAP cubes to store data
Correct ANS: B. Applying schema when data is queried
Rationale:
Schema‑on‑read allows data to be stored in its raw form and then interpreted with
a schema at the time of analysis. This flexibility is a key advantage in dealing with
heterogeneous and unstructured data in data lakes.
---
Question 4:
A financial institution is implementing a system that must support both
high‑velocity transaction processing and complex analytical queries on the same
database. Which type of database system is best suited for this hybrid workload?
A. Traditional RDBMS
B. NoSQL key‑value store
C. NewSQL or Multi‑model database
D. In‑memory OLAP
Correct ANS: C. NewSQL or Multi‑model database
Rationale:
NewSQL and multi‑model databases are designed to support both transactional
(OLTP) and analytical (OLAP) workloads without sacrificing data integrity, thereby
bridging the gap between real‑time transaction processing and complex analytics.
---
Question 5:
To combine and consolidate data from disparate sources for enterprise analytics,
which advanced data management application typically orchestrates the extraction,
©2025
Data Management Applications
4.0 Credits
Objective Assessment Review (Qns &
Ans)
2025
©2025
, Multiple Choice Questions
Question 1:
A multinational analytics firm is implementing a real‑time dashboard to monitor IoT
sensor data. Which advanced data management application is best suited to
process continuous data streams in real time?
A. Batch processing with Hadoop MapReduce
B. Stream processing platforms (e.g., Apache Kafka and Spark Streaming)
C. Traditional relational database systems
D. OLAP cubes
Correct ANS: B. Stream processing platforms (e.g., Apache Kafka and Spark
Streaming)
Rationale:
Stream processing platforms are designed to handle continuous, real‑time data
flows. They ingest and process data as it arrives, which is critical for real‑time
dashboards that monitor IoT sensors, unlike batch processing or traditional
databases that introduce latency.
---
Question 2:
An enterprise needs a scalable solution to ingest and store vast amounts of
unstructured and semi‑structured data from diverse sources. Which application
best addresses these requirements?
A. Traditional Data Warehouse
B. Data Lake
C. OLTP Database
D. In‑memory Database
Correct ANS: B. Data Lake
Rationale:
Data lakes are optimized for massive storage and ingestion of diverse data types
with schema‑on‑read capabilities, making them ideal for unstructured and
semi‑structured data. Traditional data warehouses require rigid schemas and are
less flexible for diverse data sets.
©2025
, ---
Question 3:
Which of the following best describes the schema‑on‑read approach commonly
implemented in modern data management applications?
A. Defining data schema prior to storage
B. Applying schema when data is queried
C. Enforcing a fixed schema across all data sources
D. Using OLAP cubes to store data
Correct ANS: B. Applying schema when data is queried
Rationale:
Schema‑on‑read allows data to be stored in its raw form and then interpreted with
a schema at the time of analysis. This flexibility is a key advantage in dealing with
heterogeneous and unstructured data in data lakes.
---
Question 4:
A financial institution is implementing a system that must support both
high‑velocity transaction processing and complex analytical queries on the same
database. Which type of database system is best suited for this hybrid workload?
A. Traditional RDBMS
B. NoSQL key‑value store
C. NewSQL or Multi‑model database
D. In‑memory OLAP
Correct ANS: C. NewSQL or Multi‑model database
Rationale:
NewSQL and multi‑model databases are designed to support both transactional
(OLTP) and analytical (OLAP) workloads without sacrificing data integrity, thereby
bridging the gap between real‑time transaction processing and complex analytics.
---
Question 5:
To combine and consolidate data from disparate sources for enterprise analytics,
which advanced data management application typically orchestrates the extraction,
©2025
