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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 2 of 5

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Q11.
Which factor contributes to a bottleneck in the execution of stream-based joins?

a.

Fast arrival rates of data from data sources

b.

Slow arrival rates of data from data sources

c.

High disk I/O cost for the lookup table

d.

Low disk I/O cost for the lookup table

Discuss
Answer: (c).High disk I/O cost for the lookup table Explanation:The bottleneck in stream-based join execution is often caused by the high disk I/O cost associated with the lookup table.
Q12.
What is the main research challenge related to stream-based joins in the context of real-time data warehousing?

a.

Minimizing data source arrival rates

b.

Minimizing disk I/O cost for data sources

c.

Minimizing disk I/O cost on the fast update stream

d.

Maximizing the use of slow-changing tables

Discuss
Answer: (c).Minimizing disk I/O cost on the fast update stream Explanation:The main research challenge is to minimize the disk I/O cost on the fast update stream in stream-based join execution.
Q13.
In real-time data warehousing, what is the typical purpose of stream-based joins in the ETL (extract-transform-load) layer?

a.

Sorting data streams

b.

Detecting duplicate tuples

c.

Managing data buffers

d.

Performing real-time analytics

Discuss
Answer: (b).Detecting duplicate tuples Explanation:In the ETL layer of real-time data warehousing, stream-based joins are often used for detecting duplicate tuples.
Q14.
What is the primary difference between traditional data warehousing and real-time data warehousing?

a.

Traditional data warehousing uses slow-changing tables.

b.

Real-time data warehousing processes data when it is generated.

c.

Traditional data warehousing has faster arrival rates of data.

d.

Real-time data warehousing relies on disk-based storage.

Discuss
Answer: (b).Real-time data warehousing processes data when it is generated. Explanation:The primary difference is that real-time data warehousing processes data when it is generated.
Q15.
Which type of data sources are commonly involved in stream-based joins for real-time data warehousing?

a.

Slow-changing tables

b.

Slow-moving streams

c.

Fast update streams

d.

Disk-based tables

Discuss
Answer: (c).Fast update streams Explanation:Fast update streams are commonly involved in stream-based joins for real-time data warehousing.
Q16.
What is the primary objective of the MESHJOIN algorithm?

a.

To perform real-time analytics

b.

To optimize disk-based relations

c.

To support streaming updates over persistent data

d.

To build hash tables from stream data

Discuss
Answer: (c).To support streaming updates over persistent data Explanation:The primary objective of the MESHJOIN algorithm is to support streaming updates over persistent data in the field of real-time data warehousing.
Q17.
In MESHJOIN, what serves as the build input for the hash join?

a.

The disk buffer

b.

The stream tuples

c.

The memory partitions

d.

The disk-based relation

Discuss
Answer: (b).The stream tuples Explanation:In MESHJOIN, the stream serves as the build input for the hash join.
Q18.
How does MESHJOIN handle the loading of disk-based relation segments?

a.

It loads all segments at once.

b.

It reads segments in reverse order.

c.

It reads segments sequentially in segments.

d.

It skips loading disk segments.

Discuss
Answer: (c).It reads segments sequentially in segments. Explanation:MESHJOIN reads disk-based relation segments sequentially in segments, loading one segment into the disk buffer at a time.
Q19.
What constraint ensures that a stream tuple in MESHJOIN is matched against the entire disk relation before expiring?

a.

The number of memory partitions

b.

The size of the disk buffer

c.

The total number of segments on disk

d.

The size of the disk-based relation

Discuss
Answer: (a).The number of memory partitions Explanation:The total number of memory partitions in MESHJOIN ensures that a stream tuple is matched against the entire disk relation before expiring.
Q20.
What is the key optimization in the MESHJOIN algorithm?

a.

Staggered execution of hash table build

b.

Staggered execution of disk buffer loading

c.

Direct join with the hash table

d.

Sequential loading of disk segments

Discuss
Answer: (b).Staggered execution of disk buffer loading Explanation:The key optimization in the MESHJOIN algorithm is the staggered execution of disk buffer loading, which allows efficient processing of the disk-based relation and stream tuples.
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