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Data Structures PRL Quiz 2.

Read the paper following paper to answer the quiz below.

Baumann, P., Misev, D., Merticariu, V., & Huu, B. P. (2021). Array databases: concepts, standards, implementations. Journal of Big Data, 8(1), 1-61.

https://doi.org/10.1186/s40537-020-00399-2

Question 1: What is the main reason scientific and engineering environments have only limited benefits from traditional databases?

*

A

Lack of support for complex data structures like arrays, graphs, and hierarchies.

B

Insufficient query language versatility.

C

Incompatibility with modern computational methods.

D

Lack of support for flat tables.

Question 2: What does 'transparent storage management' in the context of array databases imply?

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A

Data storage is transparently distributed across multiple servers.

B

Users can access data without knowing its physical storage details.

C

Data is transparently encrypted for security.

D

Users and administrators know the location of every byte on disk.

Question 3: What is a significant advantage of high-level query languages in array databases?

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A

They allow users to specify detailed algorithms for data processing.

B

They can translate complex queries into simple procedural code.

C

They are limited to basic arithmetic and Boolean operations.

D

They require detailed knowledge of programming.

Question 4: Why is concurrency and access control crucial in array databases?

*

A

To prevent unauthorized access and maintain data consistency.

B

To reduce the overall data storage requirements.

C

To manage user preferences and settings.

D

To prioritize data processing for administrators.

Question 5: What makes array databases particularly suited for scientific and engineering applications?

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A

They exclusively support flat table structures.

B

They offer scalable services for massive multi-dimensional arrays.

C

They restrict data access to a small number of users.

D

They are only compatible with SQL query languages.

Question 6: What is a key aspect of data model expressiveness in array systems?

*

A

Support for only 2-D and 3-D data.

B

Inability to extend the array along each dimension’s lower and upper bound.

C

Support for a wide range of dimensions and extensibility along each dimension.

D

Limitation to numeric data types only.

Question 7: What is an important factor in assessing the performance of array systems?

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A

The system’s ability to perform manual updates only.

B

The system’s restriction to procedural APIs for executing queries.

C

The architectural paradigm used, regardless of its impact on quality.

D

The tool’s speed and scalability compared to other systems.

Question 8. What role do User-Defined Functions (UDFs) play in array systems?

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A

UDFs primarily serve to restrict the expressiveness of array query languages.

B

UDFs are irrelevant in the context of array systems.

C

UDFs can be linked at runtime for invocation within the query language.

D

UDFs are used exclusively for client-side API development.

Question 9. Why is partitioning considered indispensable in array systems?

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A

It is necessary for handling arrays larger than server RAM and disk partitions.

B

It simplifies the data entry process.

C

It reduces the overall storage space required.

D

It ensures data security.

Question 10. What is the impact of lossless and lossy compression techniques in array systems?

*

A

They have a variable impact on data volume reduction.

B

They primarily enhance data security.

C

They increase the processing speed of all types of data.

D

They are mainly used for data visualization purposes.

Question 11. Which technique is used in array systems to replace query expressions with more efficient methods?

*

A

Query rewriting.

B

Data encryption.

C

Just-in-time compilation.

D

Manual query modification.

Question 12. How do full-stack, from-scratch C++ systems compare in performance to other array system architectures?

*

A

Their performance is similar to scripting language-based systems.

B

They are the slowest due to their complexity.

C

They are only effective for small data sets.

D

They are typically faster than mixed implementations and add-ons.