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MCQs | Big Data Analytics – Big Data Architecture

Big Data Analytics | Big Data Architecture MCQs: This section contains the Multiple-Choice Questions & Answers on Big Data Analytics - Big Data Architecture with explanations.
Submitted by IncludeHelp, on January 13, 2022

1. Many big data solutions prepare data for analysis and then serve the processed data in a,

  1. Structured format
  2. Unstructured format
  3. Semi-structured format
  4. None of the mentioned above

Answer: A) Structured format

Explanation:

Any big data solution prepares data for analysis before serving the processed data in a structured format that can be queried using analytical tools. The analytical data store that will be utilized to service these queries can be a relational data warehouse in the Kimball style, as is common in most traditional business intelligence (BI) solutions today.


2. The data could be presented through a low-latency _____ technology.

  1. DBMS
  2. NoSQL
  3. Data store
  4. None of the mentioned above

Answer: B) NoSQL

Explanation:

The data could be presented through a low-latency NoSQL technology such as HBase, or an interactive Hive database that provides a metadata abstraction over data files in the distributed data store.


3. Azure Synapse Analytics provides a managed service for _______.

  1. Large-scale
  2. Cloud-based
  3. Data warehousing
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Azure Synapse Analytics is a managed service for large-scale, cloud-based data warehousing that is available through Microsoft Azure. Hive, HBase, and Spark SQL are supported by HDInsight, and these databases can also be used to serve data for analysis purposes.


4. The goal of most big data solutions is to provide insights into the data through _____.

  1. Hive
  2. HBase
  3. Analysis and reporting
  4. All of the mentioned above

Answer: C) Analysis and reporting

Explanation:

The purpose of most big data solutions is to deliver insights into the data through analysis and reporting. Explanation: A data modeling layer, such as a multidimensional OLAP cube or a tabular data model in Azure Analysis Services, may be included in the design to enable users to perform data analysis on the information.


5. _____ is a form of interactive data exploration by data scientists or data analysts.

  1. Data and storage
  2. Analysis and reporting
  3. System and development
  4. None of the mentioned above

Answer: B) Analysis and reporting

Explanation:

Data scientists or data analysts can do interactive data exploration as part of their analysis and reporting processes as well. Many Azure services, such as Jupyter, include support for analytical notebooks, allowing users to leverage their existing Python or R abilities in these situations. Microsoft R Server, either solo or in conjunction with Spark, can be used for large-scale data exploration.


6. To automate the big data solutions like workflows, we use _____ technology.

  1. Orchestration
  2. HBase
  3. HDFS
  4. None of the mentioned above

Answer: A) Orchestration

Explanation:

To automate the big data solutions like workflows, we use Orchestration technology. Big data solutions are composed of recurring data processing activities that are encased in workflows that change source data, transport it across numerous sources and sinks, load it into an analytical data store, or send the results directly to a report or dashboard. Automation of these activities can be accomplished through the use of orchestration technologies such as Azure Data Factory, Apache Oozie, or Apache Hadoop and Sqoop.


7. The architecture should designed in such a way so that it can handle ______.

  1. Data ingestion
  2. Data processing
  3. Data analysis
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

The architecture must be designed in such a way that it can handle massive amounts of information being ingested, processed, and analyzed that would be impossible for traditional database management systems to handle.


8. Big data-based solutions consist of data related operations that are repetitive in nature,

  1. True
  2. False

Answer: A) True

Explanation:

In order to make use of big data, it is necessary to perform data-related operations that are repetitive in nature. These data-related operations are encapsulated in workflows, which can transform the source data, move data between sources and sinks, load data into stores, and push data into analytical units. Sqoop, oozie, data factory, and other similar tools are examples.


9. Big data services or platforms delivered through the cloud can be employed as components of a company's graph database, according to the report.

  1. True
  2. False

Answer: A) True

Explanation:

A company's big data architecture or platform (such as Azure or AWS) can be built around cloud-based services or platforms that are focused on big data. These services or platforms can even control the entire process.


10. To process large data sets quickly, big data architectures use.

  1. Distributed computing
  2. Cluster computing
  3. Parallel computing
  4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Distributed computing, also known as cluster computing, or parallel computing, is a technique for processing massive data sets quickly. It is based on multiprocessor servers performing various calculations at the same time to achieve this. It is possible to solve large issues by breaking them down into smaller components that can be solved in parallel.


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