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Dive deep into the fascinating world of Efficient Processing of Stream Data over Persistent Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Efficient Processing of Stream Data over Persistent Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Efficient Processing of Stream Data over Persistent Data, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Efficient Processing of Stream Data over Persistent Data MCQs | Page 1 of 5

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Q1.
What is the primary distinction between a traditional Database Management System (DBMS) and a Data Stream Management System (DSMS)?

a.

DBMSs store data items in memory.

b.

DSMSs store data items on disk.

c.

DBMSs perform queries over persistent data.

d.

DSMSs process stream data continuously.

Discuss
Answer: (d).DSMSs process stream data continuously. Explanation:The primary distinction between a traditional DBMS and a DSMS is that DSMSs continuously process stream data.
Q2.
What does the concept of a "window" represent in a DSMS?

a.

A window is a physical storage location for data items.

b.

A window is a sequence of operations performed on data items.

c.

A window is a snapshot of a finite set of data items at a specific point in time.

d.

A window is a buffer used for storing query results.

Discuss
Answer: (c).A window is a snapshot of a finite set of data items at a specific point in time. Explanation:In a DSMS, a "window" refers to a snapshot of a finite set of data items at a specific point in time.
Q3.
Which operation is typically NOT performed by most DSMSs?

a.

Filtering

b.

Aggregation

c.

Enrichment

d.

Indexing

Discuss
Answer: (d).Indexing Explanation:Indexing is typically not one of the common operations performed by most DSMSs.
Q4.
What is the purpose of a stream-based join in DSMSs?

a.

To store data items in memory

b.

To perform filtering operations

c.

To execute queries in a non-blocking mode

d.

To perform operations like filtering, aggregation, and enrichment

Discuss
Answer: (d).To perform operations like filtering, aggregation, and enrichment Explanation:A stream-based join in DSMSs is used to perform operations like filtering, aggregation, and enrichment.
Q5.
What are the eight essential requirements outlined by Stonebraker et al. for processing real-time stream data?

a.

Data storage, indexing, sorting, aggregation, enrichment, filtering, buffering, and querying

b.

Non-blocking mode, window management, stream-based join, snapshot creation, aggregation, enrichment, buffering, and filtering

c.

Data ingestion, disk storage, snapshot creation, aggregation, enrichment, filtering, buffering, and querying

d.

Buffer management, disk storage, window creation, stream-based join, aggregation, filtering, indexing, and querying

Discuss
Answer: (b).Non-blocking mode, window management, stream-based join, snapshot creation, aggregation, enrichment, buffering, and filtering Explanation:The eight important requirements for processing real-time stream data, as described by Stonebraker et al., are non-blocking mode, window management, stream-based join, snapshot creation, aggregation, enrichment, buffering, and filtering.
Q6.
What is a stream-based join operation primarily used for?

a.

Combining information from multiple data sources

b.

Sorting data streams

c.

Managing data buffers

d.

Performing real-time data warehousing

Discuss
Answer: (a).Combining information from multiple data sources Explanation:A stream-based join operation is primarily used for combining information from multiple data sources.
Q7.
Which application scenario is commonly associated with stream-based joins?

a.

Traditional data warehousing

b.

Near-real-time data warehousing

c.

Real-time data processing

d.

Stream-based analytics

Discuss
Answer: (b).Near-real-time data warehousing Explanation:Stream-based joins are commonly associated with near-real-time data warehousing scenarios.
Q8.
In the context of real-time data warehousing, what is the purpose of a stream-based join?

a.

To synchronize data packets in a network

b.

To perform equijoins on data streams

c.

To identify duplicate tuples in a data stream

d.

To enrich stream data with master data

Discuss
Answer: (d).To enrich stream data with master data Explanation:In real-time data warehousing, a stream-based join is used to enrich stream data with master data.
Q9.
What type of join is most commonly used in the scenario of enriching stream data with master data in real-time data warehousing?

a.

Inner join

b.

Outer join

c.

Equijoin

d.

Self join

Discuss
Answer: (c).Equijoin Explanation:Equijoin is the most natural type of join used in the scenario of enriching stream data with master data in real-time data warehousing.
Q10.
What is a key transformation in the context of stream-based joins?

a.

A transformation applied to slow-changing tables

b.

A transformation of update stream data

c.

A transformation of master data

d.

A transformation of data source keys into warehouse keys

Discuss
Answer: (d).A transformation of data source keys into warehouse keys Explanation:In stream-based joins, key transformation involves transforming data source keys into warehouse keys for data integration.
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