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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 5 of 9

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Q41.
How does sideways cracking ensure alignment of column-pairs with the same head attribute?

a.

It uses bit vectors for alignment.

b.

It performs tuple reconstruction.

c.

It maintains a log of cracking actions.

d.

It joins columns together.

Discuss
Answer: (c).It maintains a log of cracking actions. Explanation:Sideways cracking ensures alignment of column-pairs with the same head attribute by maintaining a log of cracking actions that have taken place in each pair and in other pairs that use the same head attribute.
Q42.
What is the benefit of sideways cracking in column-store systems when multiple columns of the same table are used in a query?

a.

It performs tuple reconstruction via joins.

b.

It avoids any need for tuple reconstruction.

c.

It requires indexing for efficient access.

d.

It aligns columns by default.

Discuss
Answer: (b).It avoids any need for tuple reconstruction. Explanation:The benefit of sideways cracking in column-store systems when multiple columns of the same table are used in a query is that it avoids the need for tuple reconstruction, as columns are aligned via incremental cracking and alignment actions.
Q43.
What is the purpose of partial cracking in column-store systems?

a.

To create complete columns for all data values

b.

To minimize the storage overhead of column pairs

c.

To perform updates on cracking columns

d.

To materialize all possible cracking data

Discuss
Answer: (b).To minimize the storage overhead of column pairs Explanation:The purpose of partial cracking in column-store systems is to minimize the storage overhead of column pairs by materializing only the values needed by the current hot workload set in cracking columns.
Q44.
How does partial cracking manage storage space for cracking columns?

a.

It materializes all possible values in cracking columns.

b.

It uses a strict LRU policy to manage columns.

c.

It allows each small physical column to be thrown away and recreated based on access patterns.

d.

It allocates a fixed amount of storage space for each cracking column.

Discuss
Answer: (c).It allows each small physical column to be thrown away and recreated based on access patterns. Explanation:Partial cracking manages storage space for cracking columns by allowing each small physical column to be thrown away and recreated based on access patterns, and it uses an LRU policy to decide when to do so.
Q45.
How does cracking handle updates to the data?

a.

It immediately performs updates upon arrival.

b.

It defers update actions until relevant queries arrive.

c.

It discards updates that conflict with pending queries.

d.

It relies on auxiliary columns for all updates.

Discuss
Answer: (b).It defers update actions until relevant queries arrive. Explanation:Cracking handles updates to the data by deferring update actions until relevant queries arrive. Updates are deferred until a query that is affected by a pending update arrives.
Q46.
What is the purpose of the auxiliary delete and insert columns in cracking?

a.

To store the original data values

b.

To maintain a record of deleted values

c.

To speed up query processing

d.

To store pending updates

Discuss
Answer: (d).To store pending updates Explanation:The auxiliary delete and insert columns in cracking are used to store pending updates, specifically pending deletes and inserts.
Q47.
How does cracking manage merging pending updates into cracking columns?

a.

It requires a strict order within a cracking column.

b.

It performs updates immediately upon arrival.

c.

It uses an LRU policy to decide when to merge updates.

d.

It takes advantage of the lack of strict order within a cracking column.

Discuss
Answer: (d).It takes advantage of the lack of strict order within a cracking column. Explanation:Cracking manages merging pending updates into cracking columns by taking advantage of the lack of strict order within a cracking column. Updates can be placed in any position within a cracking piece.
Q48.
What is the primary motivation for the introduction of adaptive merging as a complementary technique to cracking?

a.

To provide fast convergence to optimal performance in disk-based environments.

b.

To minimize the overhead of the initial phase of a workload.

c.

To improve sorting actions during the initial phase.

d.

To reduce the number of queries needed to reach performance levels.

Discuss
Answer: (a).To provide fast convergence to optimal performance in disk-based environments. Explanation:The primary motivation for the introduction of adaptive merging is to provide fast convergence to optimal performance in disk-based environments.
Q49.
How are data handled in adaptive merging after they are sorted in memory with a quicksort action?

a.

They are discarded.

b.

They are moved to a results column.

c.

They are merged into a single run.

d.

They are sorted again using merge sort.

Discuss
Answer: (b).They are moved to a results column. Explanation:After data are sorted in memory with a quicksort action in adaptive merging, they are moved to a results column.
Q50.
What happens if a query in adaptive merging is fully covered by the results column?

a.

The query is not executed.

b.

The query touches the initial runs.

c.

The query is executed using a binary search.

d.

The query is executed using a quicksort.

Discuss
Answer: (a).The query is not executed. Explanation:If a query in adaptive merging is fully covered by the results column, the query is not executed, as it does not need to touch the initial runs.
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