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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 7 of 9

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Q61.
What is one of the significant costs associated with data loading in a database system?

a.

Query performance

b.

Data duplication

c.

Parallel processing

d.

Time and resource overhead

Discuss
Answer: (d).Time and resource overhead Explanation:One of the significant costs associated with data loading in a database system is the time and resource overhead. Data loading involves copying and transforming data, which can be time-consuming and resource-intensive.
Q62.
In the context of Big Data, what challenge does data loading create?

a.

Data duplication

b.

High storage costs

c.

A gap between data creation and data exploitation

d.

Data corruption

Discuss
Answer: (c).A gap between data creation and data exploitation Explanation:In the context of Big Data, data loading creates the challenge of a gap between data creation and data exploitation. The time required for data loading can result in a delay before data can be effectively used for analysis or other purposes.
Q63.
What is the main challenge addressed by the adaptive loading direction in the NoDB project?

a.

Eliminating data duplication

b.

Reducing query processing time

c.

Minimizing the cost of loading data

d.

Improving data transformation efficiency

Discuss
Answer: (c).Minimizing the cost of loading data Explanation:The main challenge addressed by the adaptive loading direction in the NoDB project is to minimize the cost of loading data from raw files during query processing. The goal is to make it more efficient to access data from raw files as needed.
Q64.
Why is the external tables functionality, which attaches raw files to a database, not suitable for query processing?

a.

It requires excessive disk space.

b.

It lacks support for CSV format files.

c.

Parsing and tokenizing costs are too high.

d.

It cannot handle large datasets.

Discuss
Answer: (c).Parsing and tokenizing costs are too high. Explanation:The external tables functionality is not suitable for query processing because parsing and tokenizing costs are too high. These costs are necessary to distinguish attribute values in raw files and transform them into binary form, making query processing inefficient.
Q65.
How does the adaptive loading direction differ from traditional data loading processes?

a.

Adaptive loading happens during query processing, driven by query needs.

b.

Adaptive loading requires manual intervention to load data incrementally.

c.

Traditional data loading processes are more efficient than adaptive loading.

d.

Traditional data loading involves loading all data upfront.

Discuss
Answer: (a).Adaptive loading happens during query processing, driven by query needs. Explanation:The adaptive loading direction differs from traditional data loading processes in that it happens during query processing and is driven by the actual query needs. Traditional data loading typically involves loading all data upfront, while adaptive loading loads data incrementally based on queries.
Q66.
What advantage does the NoDB project's adaptive loading approach offer in terms of data access costs?

a.

It increases data access costs.

b.

It eliminates data access costs completely.

c.

It reduces data access costs as it learns about raw data files.

d.

It incurs a fixed data access cost for each query.

Discuss
Answer: (c).It reduces data access costs as it learns about raw data files. Explanation:The NoDB project's adaptive loading approach reduces data access costs as it learns about raw data files over time. As more queries are processed, the system gains knowledge about how data reside in raw files and can reduce the cost of accessing that data in the future.
Q67.
What is the primary benefit of NoDB's selective parsing approach when dealing with queries that involve multiple filtering conditions?

a.

It performs parsing and tokenizing for all attributes in each row.

b.

It abandons rows as soon as any filtering predicate fails, avoiding significant parsing costs.

c.

It fully loads all data before parsing, reducing overhead.

d.

It applies all filtering predicates sequentially, increasing parsing efficiency.

Discuss
Answer: (b).It abandons rows as soon as any filtering predicate fails, avoiding significant parsing costs. Explanation:The primary benefit of NoDB's selective parsing approach is that it abandons rows as soon as any filtering predicate fails, thereby avoiding significant parsing costs for rows that do not meet the filtering conditions.
Q68.
What is the purpose of the positional map index in NoDB?

a.

To provide direct access to loaded data.

b.

To track the positions of all attributes in the raw file.

c.

To identify rows that meet filtering conditions.

d.

To enable future queries to access locations in the raw file efficiently.

Discuss
Answer: (d).To enable future queries to access locations in the raw file efficiently. Explanation:The purpose of the positional map index in NoDB is to enable future queries to access locations in the raw file efficiently. It helps queries directly access a location close to what they need in the raw file.
Q69.
When does performance in NoDB reach optimal levels with respect to indexing a raw file?

a.

After indexing 100% of the raw file.

b.

After indexing 50% of the raw file.

c.

After indexing 15% of the raw file.

d.

After indexing 75% of the raw file.

Discuss
Answer: (c).After indexing 15% of the raw file. Explanation:According to experiments in Alagiannis et al. (2012), performance in NoDB reaches optimal levels after indexing approximately 15% of the raw file.
Q70.
In NoDB, what caching policy is used for cache replacement?

a.

MRU (Most Recently Used)

b.

LFU (Least Frequently Used)

c.

FIFO (First-In-First-Out)

d.

LRU (Least Recently Use

Discuss
Answer: (d).LRU (Least Recently Use Explanation:NoDB uses the LRU (Least Recently Used) caching policy for cache replacement.
Page 7 of 9

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