Exam CCA159: CCA Data Analyst Interview Questions

Cloudera is a household name in the realm of data analytics. They are regarded as important participants in the Big Data industry. Cloudera certification demonstrates that you are a subject matter expert. The CCA Data Analyst CCA159 Exam is one such certification. This exam makes your resume stand out and attracts the attention of potential employers.

Attending a data analyst interview and wondering what questions and topics you’ll face? Before attending a data analyst interview, it’s a good idea to familiarise yourself with the types of data analyst interview questions so you can mentally prepare replies for them.

In this article, we’ll look at some of the most common data analyst interview questions and answers. Data Science and Data Analytics are both thriving industries right now. Naturally, employment opportunities in these fields are expanding rapidly. The nicest aspect about pursuing a career in data science is that it provides a vast range of employment choices!

1. What exactly is the data analysis procedure?

Data analysis is the act of gathering, cleaning, interpreting, manipulating, and modeling data in order to get insights or conclusions and provide reports to assist businesses in becoming more lucrative.

2. What are the many hurdles that one faces when analyzing data?

• A Data Analyst may experience the following challenges while examining data:
• Duplicate entries and misspellings These inaccuracies might impede and impair data quality.
• Incomplete data is another key issue in data analysis. This would almost certainly result in errors or inaccurate results.
• If you are extracting data from a poor source, you will have to spend a significant amount of time cleaning it.
• Unrealistic timescales imposed by business stakeholders.

3. Describe data cleansing.

Data cleaning, also known as data cleansing, data scrubbing, or data wrangling, is the process of finding and then changing, replacing or deleting erroneous, incomplete, inaccurate, irrelevant, or missing sections of data as needed. This core data science component ensures that data is correct, consistent, and useable.

4. What tools are useful for data analysis?

Among the instruments used for data analysis are

• RapidMiner 
• KNIME 
• Google Search Operators 
• Google Fusion Tables 
• Solver 
• NodeXL 
• OpenRefine 
• Wolfram Alpha 
• io 
• Tableau, etc.

5. Explain Data mining Process.

Data mining is the process of studying data to discover previously unknown relationships. The emphasis in this scenario is on locating anomalous records, recognizing dependencies, and analyzing clusters. It also entails examining massive databases to identify trends and patterns.

6. Define Data Profiling Process

The data profiling process entails examining the data’s individual properties. The emphasis in this situation is on delivering meaningful information on data properties such as data type, frequency, and so on. It also helps with the finding and evaluation of enterprise metadata.

7. Which validation procedures do data analysts use?

It is critical to evaluate the veracity of the information as well as the quality of the source during the data validation process. Datasets can be validate in a variety of methods. Data validation methods often employed by Data Analysts include:

• Field Level Validation: This approach validates data in the field as it is entered. The mistakes can be fixed as you go.
• Form Level Validation: This type of validation occurs after the user has submitted the form. A data entry form is examined all at once, every field is validated, and any problems (if any) are highlighted so that the user can correct them.
• Data Saving Validation: When a file or database record is saved, this technique validates the data. When many data entry forms must be checked, this method is typically used.
• Validation of Search Criteria: It successfully validates the user’s search criteria in order to give accurate and related results to the user. Its primary goal is to ensure that the search results returned by a user’s query are extremely relevant.

8. Define Outlier.

Outliers in a dataset are values that differ significantly from the mean of a dataset’s characteristic attributes. We can determine either measurement variability or experimental error with the help of an outlier. Outliers are classified into two types: univariate and multivariate.

9. What are the different ways to detect outliers? Describe many approaches of dealing with it.

Box Plot Method: An outlier is defined as a number that exceeds or falls below 1.5IQR (interquartile range), that is, if it is above the top quartile (Q3) or below the bottom quartile (Q1) (Q1). The standard deviation technique defines an outlier as a value that is higher or less than the mean (3standard deviation).

10. Distinguish between data analysis and data mining.

• Firstly, data analysis is the process of obtaining, cleansing, transforming, modelling, and displaying data in order to acquire usable and relevant information that may be used to draw conclusions and determine what to do next. Data analysis has been used since the 1960s.
• Data Mining: In data mining, also known as knowledge discovery in a database, vast amounts of information are searched and analysed in order to discover patterns and laws. It has been a buzzword since the 1990s.

11. Describe the Normal Distribution.

The Normal Distribution, often known as the bell curve or the Gauss distribution, is important in statistics and serves as the foundation for machine learning. It basically specifies and measures how the means and standard deviations of a variable differ, i.e. how their values are distribute.

12. Explain how the KNN imputation algorithm works.

A KNN (K-nearest neighbor) model is one of the most common imputation algorithms. It enables the matching of a point in multidimensional space with its nearest k neighbors. Two attribute values are compare using the distance function.

13. What exactly do you mean by “data visualisation”?

A graphical depiction of information and data is referred to as data visualization. Through the use of visual elements such as charts, graphs, and maps, data visualization tools allow users to readily identify and comprehend trends, outliers, and patterns in data. With the use of this technology, data can be examine and evaluated more intelligently, and it can be translated into diagrams and charts.

14. How can data visualisation assist you?

Data visualization has gain in popularity due to the simplicity with which complex data can be seen and understood in the form of charts and graphs. It emphasizes patterns and outliers in addition to delivering data in an easier-to-understand format. The greatest representations highlight important information while reducing noise from data.

15. Mention a few Python libraries that are utilized in data analysis.

• NumPy 
• Bokeh 
• Matplotlib 
• Pandas 
• SciPy 
• SciKit

16. Explain the hash table.

Hash tables are commonly define as data structures that store data associatively. Data is typically kept in array format, allowing each data value to have a unique index value. A hash table generates an index into an array of slots from which we can obtain the desired value using the hash approach in a data analyst work.

17. What do you mean by hash table collisions? Describe how to avoid that.

Hash table collisions occur when two keys have the same index. Collisions present a difficulty since two elements in an array cannot share the same slot. To avoid such hash collisions, the following strategies can be use:

• Separate chaining technique: This method involves leveraging the data structure to hash many things to a common slot.
• Open addressing: This approach looks for vacant slots and puts the item in the first one it finds.

18. Create a list of the qualities of a good data model.

The following are some of the drawbacks of data analysis:

• Data analytics may jeopardise customer privacy and jeopardise transactions, purchases, and subscriptions.
• Tools can be complicate and necessitate prior training.
• Choosing the right analytics tool every time requires a lot of skills and expertise.
• It is possible to misuse data analytics information by targeting people with specific political ideas or nationalities.

19. List the drawbacks of data analysis in a data analyst work.

The following are some of the drawbacks of data analysis:

• Data analytics may jeopardise customer privacy and jeopardise transactions, purchases, and subscriptions.
• Tools can be complicate and necessitate prior training.
• Choosing the best analytics tool every time necessitates a great deal of knowledge and experience.
• It is possible to misuse data analytics information by targeting people with specific political ideas or nationalities.

20. Describe Collaborative Filtering.

Collaborative filtering (CF) generates a recommendation system based on user behavioral data. It filters information by analyzing data from other users and their interactions with the system. This strategy assumes that persons who agree on a certain item’s rating will most likely agree again in the future. Collaborative filtering is of three primary components: users, objects, and interests.

21. What exactly is Time Series Analysis? Where does it come into play?

A succession of data points is studied over a time interval in the field of Time Series Analysis (TSA). Instead of only recording data points intermittently or arbitrarily, analysts at the TSA capture data points at regular intervals over The TSA is essential in the following locations:

• Statistics
• The processing of signals
• Econometrics
• Forecasting the weather
• Earthquake forecasting
• Astronomy
• Science in practice

22. What exactly do you mean when you say “clustering algorithms”? What are the various features of clustering algorithms?

The process of organizing data into groups and clusters is known as clustering. It discovers related data groups in a dataset. It is a method of arranging a set of things so that the objects within the same cluster are comparable to one another rather than to those in other clusters. When implemented, the clustering algorithm has the following characteristics:

• Flat or hierarchical
• Hard or Soft
• Iterative
• Disjunctive

23. Name a few popular big data tools.

There are a few popular ones, which are as follows:

• Firstly, hadoop 
• Next, spark 
• Thirdly, scala 
• Further, hive 
• Flume 
• Last but not the least, mahout

24. Explain Clustering based on hierarchies.

This approach, also known as hierarchical cluster analysis, groups objects into clusters based on similarities. When we execute hierarchical clustering, we get a set of clusters that are distinct from one another.

25. Explain the N-gram.

The probabilistic language model, often known as the N-gram, is defined as a sequence of n things in a given text or speech. It is essentially made up of neighboring words or letters of length n from the original text. In other terms, it is a method of predicting the next item in a sequence, like in (n-1).

26. What do you mean when you say logistic regression?

Logistic Regression is a mathematical model that can be use to investigate datasets having one or more independent variables that influence a specific outcome. The model predicts a dependent data variable by evaluating the connection between various independent factors.

27. What is the K-means algorithm?

K-mean is one of the most well-known partitioning algorithms. The unlabeled data is clustered using this unsupervised learning approach. ‘k’ denotes the number of clusters. It makes an effort to keep each cluster isolated from the others. There will be no labels for the clusters to operate with because it is an unsupervised model.

28. Define Data Warehouse.

Data Warehouse: This is an appropriate location for storing all of the data gathered from various sources. A data warehouse is a centralised repository of data that stores information from operational systems and other sources. In mid- and large-sized businesses, it is a standard technique for integrating data across team- or department-silos. It gathers and maintains data from various sources in order to deliver useful business insights.

29. What is Data Lake?

A data lake is essentially a huge storage device that saves raw data in its original format. Analytical performance and native integration are improved as a result of the vast volume of data. It takes use of data warehouses’ main flaw: their inability to be flexible. There is no need for planning or knowledge of data analysis in this case; the analysis is to take place afterwards, on-demand.

30. What are the benefits of employing version control?

Version control, often known as source control, is a system for configuring software. This can be use to handle records, files, datasets, or papers. The following are the benefits of version control:

• Version control allows for the analysis of deletions, edits, and creations of datasets since the original copy.
• With this strategy, software development becomes more apparent.
• It aids in distinguishing between different versions of the document.
• It keeps a complete history of project files, which is useful in the event that the central server fails.
• This utility makes it simple to securely store and maintain numerous versions and variants of code files.
• It allows you to see the modifications made to various files.

Conclusion for Exam CCA159: CCA Data Analyst Interview Questions

Now that you are aware of the various data analyst interview questions that might be asked in an interview, you will find it easier to prepare for your interviews. You examined numerous data analyst interview questions depending on complexity levels, tools, and programming languages in this section.