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CompTIA Data+ (DA0-001) Interview Questions

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CompTIA Data+ (DA0-001) Interview Questions

Data scientists are in demand, and CompTIA Data+ (DA0-001) certification validates the skills you need to help promote data-driven decisions. To pass the interview you ought to showcase your professionalism in analyzing and interpreting data. Further, you will also need to demonstrate that you can mine the company data more effectively, thus making rewarding business decisions. You must prove to the panel that by hiring you as a certified professional, the organization can avoid confounding results. Additionally, if you want to revise the concepts and know about other preparation resources, you can go through the CompTIA CASP+ (CAS-004) Online tutorial as well. 

You will need to show the hiring manager that you have the skills required and that you are a capable communicator. In addition, you must handle yourself well during the interview. Here are some CompTIA Data+ (DA0-001) questions you might encounter during your interview. Let’s get started!

1. What are the typical sources of data which is used for data analytics?

Raw data can be collected through various sources such as computers, online sources, cameras, environmental sources, or personnel. Further, once the data is collected, it must necessarily be organized so that it can be analyzed. This may take place on a spreadsheet or another form of software that can process statistical data.

2. Could you name the dimensions of the data?

  • Accuracy
  • Completeness
  • Consistency
  • Timeliness
  • Validity
  • Uniqueness

3. What are the five main data types?

The five basic categories of data types in modern programming languages are: 

  • Integral
  • Floating Point
  • Character
  • Character String
  • Composite types

4. How would you define the derived data types?

A derived data type is comprised of the fundamental data type and some aggregation of it. Data types including Void, Float, Integer, and Character are all fundamental data types. Whereas, structures, Unions, Arrays, and Pointers are the derived data types.

5. What is float data type?

Integers are simply the whole numbers that can be positive or negative – that is, they are not fractions. Integers range from -2147483648 to 2147483647. Floats have a decimal point of someplace and provide more precision than integers. They are used when fractional quantities are needed. Rounding is performed on floats to arrive at an integer-based value of a float data type.

6. Could you name the common types of data types and structures?

  • Linear: arrays, lists.
  • Tree: binary, heaps, space partitioning, etc.
  • Hash: distributed hash table, hash tree, etc.
  • Graphs: decision, directed, acyclic, etc.

7. Can you tell me which data structure is best for the file directory?

Most directories are structured in a tree structure. When it comes to keeping track of directories and files on a computer, the B+ tree is the best option.

8. What is the difference between data structure and file structure?

Data structures are that part of computer science as well as computer engineering which mainly focuses on data storage and retrieval. Data structure presents the set of techniques that can be used to store & retrieve data from primary memory or secondary memory in an efficient manner. A file structure is the logical collection of information that resides on a computer storage device, such as a hard disk, flash drive, and magnetic tape. 

9. What are the four data mining techniques?

  • Regression (predictive)
  • Association Rule Discovery (descriptive)
  • Classification (predictive)

10. Could you tell me some of the challenges of data mining?

  • Security and Social Challenges.
  • Noisy and Incomplete Data.
  • Distributed Data.
  • Complex Data.
  • Performance.
  • Scalability and Efficiency of the Algorithms.
  • Improvement of Mining Algorithms.
  • Incorporation of Background Knowledge.

11. How does data profiling differ from data cleansing?

Data cleansing is nothing but the process where you apply the findings of data profiling with the aim of standardizing the data and removing anomalous patterns. As opposed to data profiling, which analyzes your source data. 

12. Why is data profiling important?

Data profiling can reveal a wide variety of issues and problems with the data, from possible corruption to inconsistent or inappropriate formatting. In the absence of such analysis, you risk loading bad data into your repository, which will then wreak havoc with any system that uses that data.

13. Could you tell me the basic types of data manipulation?

  • Moving the data around unchanged
  • Carrying out arithmetic operations on data
  • Testing data
  • Carrying out logic operations on data.

14. What are the most commonly used tools for manipulating data?

  • MicroStrategy Analytics Express.
  • NodeXL.
  • Content Analysis Tools.
  • Qualtrics.
  • Microsoft’s Data Explorer for Excel (Beta)

15. What do you know about query optimization techniques?

SQL Query optimization, in the context of database systems, is defined as the iteration of various methods in order to reduce the cost associated with a query. Cost measuring criteria vary across databases but can include execution time and unnecessary disk accesses.

16. What are the two techniques for implementing query optimization?

  • Cost-based Optimization (Physical): This technique is based on the cost of the query. There are different ways to answer a query depending on the indexes, constraints, sort methods, etc.
  • Heuristic Optimization (Logical): also known as rule-based optimization.

17. Why is query optimization so important?

Query optimization is an important process in database management because: 

  • Query optimization decreases the cost per query and increases the performance of the system
  • It uses less memory from the databases
  • It gives less stress to the database

18. What are the 4 types of descriptive statistics?

  • Measures of Frequency: * Count, Percent, Frequency
  • Measures of Central Tendency. * Mean, Median, and Mode
  • Measures of Dispersion or Variation. * Range, Variance, Standard Deviation
  • Measures of Position. * Percentile Ranks, Quartile Ranks.

19. Is descriptive statistics qualitative or quantitative?

Descriptive statistics are nothing but just numbers used to summarize and describe data. However, it does not let us draw any conclusions about hypotheses that we have made or draw any conclusions about the data we have analyzed. They are a simple way of describing the data.

20. Could you elaborate on the purpose of inferential statistics?

Inferential statistics attempt to draw conclusions or make predictions about a large group of people by studying a smaller sample of that population. The hope is that the results we learn from the smaller sample will generalize to the larger population.

 

21. Which tool is best for data analytics?

  • Python.
  • R.
  • SAS.
  • Excel.
  • Power BI.
  • Tableau.
  • Apache Spark.

22. What are the key design principles for dashboards?

  • determining the needs of your users
  • selecting the right type of dashboard
  • providing immediate access to relevant info
  • selecting the right type of data visualization
  • keeping the design simple and easy to understand.

23. Can you name the elements of a dashboard?

An effective dashboard contains three elements, namely:

  • Know Your Audience: Determining which content and details must be included in the dashboard.
  • Tell A Story: Presenting the findings in an intuitive way and also illustrating the ones that support your statements.
  • Leads to Action: Making connections and answering questions.

24. What do you know about data visualization and why is it important?

Interpreting data is easier when it is represented as a chart, graph, or table. Data visualization makes it possible to easily identify patterns and trends from large sets of data. Additionally, visualizations make it easy to spot outliers. When data is presented visually it is often easier for the human eye to detect patterns, trends, and outliers.

25. Could you name the common types of data visualization?

  • Scatter plots
  • Line graphs
  • Pie charts
  • Bar charts
  • Heat maps
  • Area charts
  • Choropleth maps
  • Histograms

26. How would you define data governance?

The process of data governance is used to guide data management practices through the entire lifecycle and ownership process. It helps to manage, utilize, and protect all digital and hard copy assets. The business goals of your organization will help to inform best practices for data governance.

27. Can you explain what is the meaning of data quality?

Data quality is a measure of how well data is documented, which helps to determine if it’s reliable for use in decision-making. By setting organizational standards for data documentation and quality, data scientists, managers, and executives can use high-quality data sets repeatedly to inform strategic business decisions with confidence.

28. What do you know about the  MDM system?

Mobile Device Management (MDM) enables IT Managers to control and secure mobile devices through a central console. MDM allows IT managers to remotely configure, monitor, and secure company assets such as corporate data and applications. By providing an integrated solution for multiple platforms, customers can easily navigate the complexities of device management at scale with minimal effort.

29. What are the four types of MDM?

  • Consolidation
  • Registry
  • Centralized
  • Coexistence

30. Why do you think is MDM needed?

MDMs, or mobile device managers, can help companies control the mobile devices they distribute to employees. MDMs can help IT ensure that data is transmitted over a secure Wi-Fi connection, and restrict access to sensitive information, providing user authentication to keep data even safer. MDMs also offer remote data wiping of devices if they become lost or stolen.

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