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Welcome to the Big Data Exploration MCQs Page

Dive deep into the fascinating world of Big Data Exploration with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Exploration, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Exploration, 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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Check out the MCQs below to embark on an enriching journey through Big Data Exploration. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Data Exploration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Exploration MCQs | Page 4 of 9

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Q31.
How does a column store's data access during query processing differ from traditional row stores?

a.

Column stores access all data at once.

b.

Column stores access only the referenced data/columns during query processing.

c.

Column stores process full tuples one at a time.

d.

Column stores rely on random data access.

Discuss
Answer: (b).Column stores access only the referenced data/columns during query processing. Explanation:In column stores, only the referenced data/columns are accessed during query processing, which is different from traditional row stores.
Q32.
What kind of processing do column stores typically rely on?

a.

Tuple-wise processing

b.

Random access processing

c.

Bulk and vector-wised processing

d.

Sequential access processing

Discuss
Answer: (c).Bulk and vector-wised processing Explanation:Column stores typically rely on bulk and vector-wise processing.
Q33.
What does the term "cracking" in the context of database cracking reflect?

a.

The process of breaking the database into smaller pieces.

b.

The act of repairing data in a database.

c.

The act of clustering data based on a query predicate.

d.

The process of compressing data in a column store.

Discuss
Answer: (a).The process of breaking the database into smaller pieces. Explanation:In the context of database cracking, the term "cracking" reflects the process of breaking the database into smaller and manageable pieces.
Q34.
In the context of database cracking, what does the system do with the base column after the first query on a column?

a.

It deletes the base column.

b.

It copies the base column to an auxiliary column.

c.

It creates a new table for the base column.

d.

It compresses the base column.

Discuss
Answer: (b).It copies the base column to an auxiliary column. Explanation:In the context of database cracking, after the first query on a column, the system copies the base column to an auxiliary column where all cracking happens.
Q35.
What data structure is used to maintain partitioning information in cracking?

a.

Hash table

b.

AVL-tree

c.

Linked list

d.

B-tree

Discuss
Answer: (b).AVL-tree Explanation:In cracking, an AVL-tree is used to maintain partitioning information, such as which pieces have been created and which values have been used as pivots.
Q36.
What is the benefit of continuously reorganizing columns through cracking in response to queries?

a.

It minimizes the storage space required for columns.

b.

It eliminates the need for query optimization.

c.

It gradually improves query performance as more queries are processed.

d.

It ensures that columns are always fully sorted.

Discuss
Answer: (c).It gradually improves query performance as more queries are processed. Explanation:Continuously reorganizing columns through cracking in response to queries gradually improves query performance as more queries are processed.
Q37.
Why does cracking stop cracking a column for pieces smaller than L1 cache?

a.

Because smaller pieces are more efficient to search without reorganizing.

b.

Because smaller pieces pose a risk of data loss.

c.

Because smaller pieces cannot be cracked.

d.

Because smaller pieces have no performance benefit.

Discuss
Answer: (a).Because smaller pieces are more efficient to search without reorganizing. Explanation:Cracking stops cracking a column for pieces smaller than L1 cache because smaller pieces are more efficient to search without reorganizing, and the benefit of cracking such small pieces is minimal.
Q38.
In column-store systems, what allows for efficient query processing when requesting multiple columns of the same table?

a.

Indexing

b.

Tuple reconstruction

c.

Random access

d.

Physical reorganization

Discuss
Answer: (b).Tuple reconstruction Explanation:In column-store systems, efficient tuple reconstruction allows for efficient query processing when requesting multiple columns of the same table.
Q39.
When a column is cracked in a column-store system, what happens to the alignment of columns within the same table?

a.

They remain aligned.

b.

They become more efficiently aligned.

c.

They are misaligned.

d.

They are automatically sorted.

Discuss
Answer: (c).They are misaligned. Explanation:When a column is cracked in a column-store system, the columns within the same table are no longer aligned; they become misaligned.
Q40.
How does sideways cracking address the problem of misalignment in column-store systems when working with multiple columns of the same table?

a.

It cracks all columns of the same table in the same way.

b.

It performs random access for tuple reconstruction.

c.

It works on pairs of columns at a time and forwards cracking actions across columns.

d.

It uses bit vectors to align columns.

Discuss
Answer: (c).It works on pairs of columns at a time and forwards cracking actions across columns. Explanation:Sideways cracking addresses the problem of misalignment in column-store systems when working with multiple columns of the same table by working on pairs of columns at a time and forwarding cracking actions across columns.
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