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Introduction to Data Analytics Interview Questions

So you have finally found your dream job in Data Analytics but are wondering how to crack the 2023 Data Analytics interview and what the probable Data Analytics Interview Questions could be. Every Data Analytics interview and the job scope are different too. Keeping this in mind, we have designed the most common Data Analytics Interview Questions and answers to help you get success in your Data Analytics interview.

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Below are the Top 2023 Data Analytics Interview Questions primarily asked in an interview. These are divided into two parts.

Part 1 – Data Analytics Interview Questions and Answers (Basic)

Below are the basic interview questions and answers:

Q1. What is the difference between Data Mining and Data Analysis?

Answer:

Data Mining Data Analysis

A hypothesis is not required for Data Mining. Data analysis begins with a hypothesis.

Data Mining demands clean and well-documented data. Data analysis involves data cleaning.

The results of data mining are not always easy to interpret. Data analysts interpret the results and present them to the stakeholders.

Data mining algorithms automatically develop equations. Data analysts have to develop their equations.

Q2. Mention what are the various steps in an analytics project.

Answer:

Data analytics involves collecting, cleansing, transforming, and modeling data to gain valuable insights and support better organizational decision-making.

The steps involved in the data analysis process are as follows:

Data Exploration: Having explored the business problem, a data analyst has to analyze the root cause of the problem.

Data Preparation: In this step of the data analysis process, we find data anomalies like missing values within the data.

Data Modelling: The modeling step begins after the data has been prepared. Modeling is an iterative process wherein the model runs repeatedly for improvements. Data modeling ensures the best possible result for a business problem.

Validation: In this step, the model is provided by the client, and the model developed by the data analyst are validated against each other to find out if the developed model will meet the business requirements.

Implementation of the Model and Tracking: In this final step of the data analysis, model implementation is done, and after that, tracking is done to ensure that the model is implemented correctly or not.

Q3. What is the responsibility of a Data Analyst?

Answer:

Resolve business-associated issues for clients and perform data audit operations.

Interpret data using statistical techniques.

Identify areas for improvement opportunities.

Analyze, identify, and interpret trends or patterns in complex data sets.

Acquire data from primary or secondary data sources.

Maintain databases/data systems.

Locate and correct code problems using performance indicators.

Securing database by developing access system.

Q4. What is Hash Table Collisions? How is it Avoided?

Answer:

A hash table collision happens when two different keys hash to the same value. There are many techniques to avoid hash table collision; here, we list two.

Separate Chaining: It uses the data structure that hashes to the same slot to store multiple items.

Open Addressing: It searches for other slots using a second function and store item in the first empty slot.

Q5. List some best tools that can be useful for data analysis.

Tableau

RapidMiner

OpenRefine

KNIME

Google Search Operators

Solver

NodeXL

io

Wolfram Alpha’s

Google Fusion Tables

Q6. What is the difference between data mining and data profiling?

Answer:

The difference between data mining and data profiling is as follows:

Data profiling: It targets the instant analysis of individual attributes like price vary, special price and frequency, the incidence of null values, data type, length, etc.

Data mining: It focuses on dependencies, sequence discovery, relation holding between several attributes, cluster analysis, detection of unusual records, etc.

Part 2 – Data Analytics Interview Questions and Answers (Advanced) Q7. Explain K-mean Algorithm and Hierarchical Clustering Algorithm.

Answer:

K-Mean Algorithm: K mean is a famous partitioning method. In the K-mean algorithm, the clusters are spherical, i.e. the data points in a cluster are centered on that cluster. Also, the variance of the clusters is similar, i.e., each data point belongs to the closest cluster.

Hierarchical Clustering Algorithm: Hierarchical clustering algorithm combines and divides existing groups and creates a hierarchical structure to show the order in which groups are divided.

Q8. What is data cleansing? Mention a few best practices you must follow while doing data cleansing.

Answer:

Sorting the information required for data analysis from a given dataset is essential. Data cleaning is a crucial step wherein data is inspected to find anomalies, remove repetitive and incorrect information, etc. Data cleansing does not involve removing any existing information from the database; it just enhances the data quality for analysis.

Developing a data quality plan to identify where maximum data quality errors occur so that you can assess the root cause and plan according to that.

Follow a customary method of substantiating the necessary information before it’s entered into the information.

Identify any duplicate data and validate the accuracy of the data, as this will save a lot of time during analysis.

Tracking all the improvement operations performed on the information is incredibly necessary so that you repeat or take away any operations as required.

Q9. What are some of the statistical methods that are useful for data-analyst?

Answer:

Statistical methods that are useful for a data scientist are:

Bayesian method

Markov process

Spatial and cluster processes

Rank statistics, percentile, outlier’s detection

Imputation techniques, etc

Simplex algorithm

Mathematical optimization

Q10. Explain what imputation is. List out different types of imputation techniques. Which imputation method is more favorable?

Answer:

During imputation, we tend to replace missing information with substituted values.

The kinds of imputation techniques involve are:

Single Imputation: Single imputation denotes that a value replaces the missing value. In this method, the sample size is retrieved.

Hot-deck imputation: A missing value is imputed from a randomly selected similar record by using a punch card

Mean imputation: It involves replacing the missing value with the predicted values of other variables.

Regression imputation: It involves replacing the missing value with the predicted values of a particular value depending on other variables.

Stochastic regression: It is the same as regression imputation but adds the common regression variance to the imputation.

Multiple imputation: Unlike single imputation, multiple imputations estimate the values multiple times.

Although single imputation is widely used, it does not reflect the uncertainty created by missing data at random. So, multiple imputations are more favorable than single imputations in case of data missing at random.

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Top Google Bigquery Frequently Asked Interview Questions

Introduction

Suppose you are appearing in an interview for the Junior or senior role. In that case, it’s important to have a basic understanding of GCP and BigQuery. So, in this article, you will learn interview questions related to GCP.

You can start introducing BigQuery: “It is a powerful cloud-based data warehousing solution that can handle large-scale data processing tasks, including machine learning, predictive analytics, data visualization, and real-time data streaming.”

Example: 

You might be asked to share a specific example of a business problem you solved using BigQuery, and prepare recent work and projects.

Note: These questions are just a few examples of the types of questions you might encounter during a GCP BigQuery interview, and answers may vary from person to person.

Table of Contents Q1. How does BigQuery differ from traditional data warehousings solutions like Oracle or SQL Server?

We can differentiate BigQuery from traditional data warehousing solutions in a few ways,

You start querying data right away without setting up infrastructure in BigQuery.

It handles large datasets and processes queries quickly using a distributed architecture. It’s serverless, so we don’t need to manage servers or infrastructure.

BigQuery is a modern cloud-based solution that allows for more flexibility and scalability than traditional data warehousing solutions and is easier to use and manage.

Q2.  How do you manage data security and privacy, especially when dealing with sensitive data?

To manage data security and privacy in BigQuery, you can explain to the interviewer:

Limit access with IAM roles

Encrypt data in transit and at rest

Enable audit logging, use data masking

Check for compliance certifications

Establish data retention policies.

We can help ensure our sensitive data’s confidentiality, integrity, and availability in BigQuery.

Q3.  How do you design a schema for a complex data model, such as a hierarchical or graph database?

Designing a BigQuery schema for a complex data model, such as a hierarchical or graph database, requires careful consideration of the data structure and relationships.

To design a BigQuery schema for a complex data model, you can explain to the interviewer:

Identify entities and relationships

Normalize the data

Choose an appropriate schema type

Optimize for query performance

Test and iterate as needed.

Q4.  How do you handle streaming data, and what are some best practices for real-time data processing?

We can use BigQuery’s streaming inserts, choose the appropriate data ingestion method, optimize data ingestion, optimize query performance, and implement real-time monitoring and alerting. By implementing these best practices, we can ensure that our real-time data is processed efficiently and accurately and that issues are detected and resolved quickly.

Integrating BigQuery with other data processing tools, like Apache Spark or Apache Beam, can help us to perform complex data analysis tasks. We can use BigQuery’s connectors, APIs, third-party tools, or data transfer services to integrate with these tools. By integrating BigQuery with other data processing tools, we can simplify and enhance our data processing and analysis capabilities.

Source: GCP

Q6.  How do you use BigQuery ML to perform machine learning tasks like regression or classification?

To perform machine learning tasks using BigQuery ML, we must prepare our data, choose a model type, create and train it using SQL statements, evaluate its performance, and make predictions. By following the below steps, We can perform machine learning tasks within BigQuery and gain insights from our data more efficiently.

Q7.  How do you monitor performance and usage?

We can track query performance with execution time, bytes processed, and slot usage also; we can Monitor CPU, memory, and network throughput for resource usage. We can also track job completion time, error rates, and concurrency for BigQuery operations.

Q8.  How do you handle versioning, and what are some best practices for data version control?

Version control can be managed using the BigQuery Data Catalog, source control tools like Git, and by maintaining clear documentation of our data pipeline and transformation processes. Best practices for data version control involve using tools like the BigQuery Data Catalog and source control tools, along with maintaining clear documentation of the data pipeline and transformation processes.

It can be used for data visualization and reporting by connecting it with visualization tools like Google Data Studio, Looker, Tableau, or Power BI. These tools allow us to create custom dashboards and reports by querying data directly from BigQuery. Common visualization techniques include creating charts, graphs, tables, and other interactive visualizations to help communicate insights from our data.

Conclusion

We covered a variety of questions related to GCP BigQuery. Understanding best practices for designing efficient schemas, managing data security and privacy, monitoring performance and usage, troubleshooting common issues, integrating with other data processing tools, and handling data from different sources and regions is important.

Key Takeaways:

Understanding how to optimize query performance, including techniques such as partitioning, clustering, and using appropriate data types.

Following best data security and privacy practices, such as using encryption and access controls to protect sensitive data.

Monitoring performance and usage metrics to identify bottlenecks and optimize resources.

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Top Mongo Database Interview Questions And Answers In 2023

Mongo Database Interview Questions and Answers

Mongo database interview questions allow interviewers to test candidates’ knowledge, especially software developers and database administrators, in the domain of MongoDB DBMS.

With the popularity of “MERN” (MongoDB, Express, ReactJS, chúng tôi and “MEAN” (MongoDB, Express, AngularJS, Node.js)  stack web development, the popularity of MongoDB has skyrocketed as a requirement for candidates. As such, the average base salary of a software engineer with knowledge of MongoDB is $91,524 per year. Furthermore, senior developers can earn up to $159,000, and senior database administrators earn a base salary of $165,000 annually.

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MongoDB helps efficiently store data for content management systems and web applications and supports several programming languages. Hence, several job positions require MongoDB proficiency. Preparing before the interview with Mongo database interview questions can help the candidate to answer well and obtain an exciting job with a lucrative salary.

Part 1 – Mongo Database Interview Questions 1. What is MongoDB?

Answer:

MongoDB is a popular open-source NoSQL document-oriented database management system.

MongoDB stores data in flexible, JSON-like documents, allowing for easy data modeling and flexible schema design.

It supports dynamic schema, meaning fields can be added to documents without defining a rigid structure.

MongoDB is widely used in modern web applications, content management systems, mobile apps, real-time analytics, and many other use cases.

2. Mention unique features of Mongo Database.

Answer:

Indexing: MongoDB supports generic secondary indexes, allowing a variety of fast queries and providing unique, compound, geospatial, and full-text indexing capabilities.

Aggregation: MongoDB supports an “aggregation pipeline” that allows you to build complex aggregations from simple pieces and will enable the database to optimize them.

Special collection types: MongoDB supports time-to-live collections for data that should expire at a particular time, such as sessions. It also helps fixed-size collections and holds recent data, such as logs.

File storage: MongoDB supports an easy-to-use protocol for storing large files and metadata.

3. What is the command for starting MongoDB?

Answer:

mongod

mongod –help for help and startup options

4. How to represent a null value in a variable in MongoDB?

Answer:

{"x" : null} 5. Write down the code to connect to MongoDB

Answer:

var connectTo = function(port, dbname) { if (!port) { port = 27017; } if (!dbname) { dbname = "test"; } db = connect("localhost:"+port+"/"+dbname); return db; }; 6. What is GridFS in MongoDB?

Answer:

GridFS is a mechanism for storing and retrieving large files in MongoDB.

It is a file system abstraction built on top of MongoDB.

GridFS stores files in two collections – one for metadata and another for binary data.

Files are divided into chunks and stored in 255KB chunks.

GridFS helps handle large files and distribute them across multiple shards.

It allows easy management and retrieval of files using the same MongoDB query language and drivers.

7. What are the benefits of Mondo DB?

Answer:

8. Write down the syntax for string expression in MongoDB?

Answer:

"$substr" : [expr, startOffset, numToReturn] 9. What is MapReduce in MongoDB?

Answer:

MapReduce is a powerful and flexible tool for aggregating data.

It can solve problems too complex to express using the aggregation framework’s query language.

MapReduce uses JavaScript as its “query language” to express arbitrarily complex logic.

MapReduce tends to be slow and is not ideal for real-time data analysis.

Part 2 – Mongo Database Interview Questions (Advanced) 10. Write the difference between normalization and denormalization?

Normalization means dividing up data into multiple collections with references between collections.

Each piece of data lives in one collection, although multiple documents may reference it.

Thus, to change the data, only one document must update. However, MongoDB has no joining facilities, so gathering documents from multiple collections will require various queries.

Denormalization is the opposite of normalization: embedding all the data in a single document.

Instead of documents containing references to one definitive copy of the data, many documents may have copies of the data.

This means that multiple documents need to update if the information changes but that a single query can fetch all related data.

11. What is cardinality?

Answer:

Cardinality means how many references a collection has to another collection. Common relationships are one-to-one, one-to-many, or many-to-many.

One-to-one relationship: A single record in one collection has an association with only one record in another collection.

One-to-many relationship: A single record in one collection has an association with multiple records in another collection.

Many-to-many relationship: Multiple records in one collection have associations with multiple records in another collection.

12. What are the limitations of MongoDB?

Answer:

MongoDB does not offer traditional joins. Instead, it provides “lookups,” which are slower in execution.

It does not support transactions across multiple clusters.

Compared to traditional RDBMS, it consumes more memory.

It has a limit on the number of indexes.

It has limited support for complex transactions with multiple operations.

13. What is replication in MongoDB?

Answer:

Replication is a way of keeping identical copies of data on multiple servers.

It is ideal for all production deployments.

Replication keeps the application running and its data safe, even if something happens to one or more servers.

With MongoDB, one can set up replication by creating a replica set.

A replica set is a group of servers with one primary (the server taking client requests) and multiple secondary servers that keep copies of the primary’s data. If the primary crashes, the secondaries can elect a new primary from amongst themselves.

14. What is the command used to set replication in MongoDB?

Answer:

replicaSet = new ReplSetTest({"nodes" : 3}) 15. What is rollback? When does rollback fail in MongoDB?

Answer:

Rollback is the process of undoing changes made to a database.

Rollback ensures that the database is consistent and does not contain any incomplete or incorrect data.

It helps to restore data to a previous state when a transaction encounters an error.

Rollback can fail in MongoDB if a user has already committed a transaction or if there are network or system failures during the rollback process.

Rollback may also fail if multiple transactions are in progress simultaneously and the user attempts to roll back one while another is still in progress.

MongoDB decides that the rollback is too significant to undertake. Rollback can fail if there are more than 300 MB of data or about 30 minutes of operations to roll back. In these cases, you must re-sync the node stuck in rollback.

16. What is Sharding in MongoDB?

Answer:

Sharding refers to splitting data up across machines.

The term partitioning is also sometimes used to describe this concept.

Putting a subset of data on each machine makes it possible to store more data and handle more load without requiring more powerful machines, just a larger quantity of less-powerful machines.

17. What is Manual Sharding?

Answer:

Manual sharding is when an application maintains connections to several different database servers, each of which is entirely independent.

The application stores different data on different servers and queries against the appropriate server to get data back.

This approach can work well but becomes difficult to maintain when adding or removing nodes from the cluster or in the face of changing data distributions or load patterns.

Final Thoughts Frequently Asked Questions (FAQs)

1. What are the interview questions for MongoDB?

Answer: MongoDB interview questions may cover various topics, such as data modeling, indexing, query optimization, aggregation, administration, security, and scaling.

2. What type of database is MongoDB?

Answer: MongoDB is a NoSQL document-oriented database.

3. What is MongoDB used for?

Answer: MongoDB is used for storing and retrieving data in a flexible and scalable manner. It is commonly used in web applications, real-time analytics, content management systems, and mobile apps.

4. Why use MongoDB over SQL?

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Top 30 Pig Interview Questions And Answers {Updated For 2023}

Introduction To Pig interview Question and Answers

Apache Pig is a high-level platform for which is used to create programs that run on Hadoop. The Language of Pig is known as Pig Latin. Pig is written in Java, and it was developed by Yahoo research and Apache software foundation. Its initial release happened on 11 September 2008. Preparing for a job interview in Pig. I am sure you want to know the most common 2023 Pig Interview Questions and answers that will help you crack the Pig Interview with ease.

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Below is the list of top Pig Interview Questions and answers at your rescue. These interview questions are divided into two parts are as follows:

Part 1 – Pig Interview Questions (Basic) Q1.What is the difference between Map-Reduce and Pig?

Map Reduce is a compiled language, and the code efficiency of Map-reduce is high, and Pig is a scripting language with less code efficiency.

Q2.What do you mean by the bag in Pig?

The collection of tuples is known as a bag in a pig.

Q3.What are the complex data types in Pig?

Map, Tuples, and Bag are the complex data types of Pig.

Q4.What is flatten in Pig?

When we want to remove the nesting from the data in a tuple or bag, then we use Flatten.

Q5.Suppose we have a file name with chúng tôi and having the attribute like id, name, year, rating, duration. How will you upload this file to a pig?

movies= LOAD ‘path of abc.csv’ USING Pig Storage(‘,’) as (id,name,year,rating,duration);

Q6.What is the difference between PigLatin and HIVEQL?

HIVEQL is a declarative language, and PigLatin is a procedural mail.

Let us move to the next Pig Interview Questions.

Q7.What do you mean by an inner bag and outer bag in a pig?

The relation inside the bag is referred to as the inner bag, and the normal relationship is known as an Outer bag.

Q8.What is the difference between Group and COGROUP?

GROUP operator is used to group the data in a single relation, and COGROUP is used to make the relation in GROUP and JOIN.

Q9.What is the difference between COUNT and COUNT_STAR?

COUNT function doesn’t work with a NULL value when we are counting an element in a bag, but COUNT_STAR will consider the NULL value.

Q10. What are the diagnostic operators available in Apache Pig?

Dump Operator, Describe Operator, Explain Operator, Illustrate operator.

Q11.What do you mean by UNION and SPLIT operator?

By using a UNION operator, we can merge the contents of two or more relations and a SPILT operator is used to divide the single relation into two or more relations.

Q12.How to get the top 10 tuples from the relation R?

By using the TOP () function.

Let us move to the next Pig Interview Questions.

Q13.What are the similarities between Pig and Hive?

Pig use PigLatin and Hive use HiveQL both converts the commands into MapReduce jobs.

Q14.what are the different types of UDF’s functions of JAVA that Apache Pig supports? Q15.You have a file chúng tôi in the HDFS directory with 1000 records. You want to see only the first 10 records from the chúng tôi file. How will you do this?

Result= limit employee 10

Part 2 – Pig Interview Questions (Advanced) Q16.How do users interact with Hadoop in Pig?

By using grunt shell

Q17.Is Pig support multi-line commands?

Yes

Q18.What are all stats classes in a pigstats package?

PigStats, JobStats, OutputStats, InputStats.

Q19.What is UDF?

The function which is not a built-in operator but can programmatically create a function to bring up the functionality.

Q20.Explain is the case sensitivity in Pig Latin?

The functions and names of relations are cases sensitive in Pig Latin, but a name or keyword and parameter are case insensitive.

Q21.What is Grunt in Pig?

Grunt is a command terminal which is an interactive shell where we give the command of Pig.

Q22.What is the requirement of MapReduce in Pig programming?

MapReduce is an execution engine.

Q23.What is a Pig engine?

The pig engine provides the execution environment to run the pig programs. It converts the pig operations into MapReduce jobs.

Q24.What are the execution modes of Pig?

MapReduce Mode: Execution will be done of the Hadoop cluster.

Q25.What are the different Eval functions available in a pig?

AVG, CONCAT, MAX, MIN, SM, SIZE, COUNT are different EVAL pig functions.

Q26.What do you mean by LOAD and STORE in Pig?

These are the operator for loading and storing the data in hdfs.

Let us move to the next Pig Interview Questions.

Q27.Which Math function available in Pig?

ABS, ACOS, LOG, ROUND, CBRT, SORT are the math functions available in Pig.

Q28.What did the distinct keyword do in Pig?

New_movies= distinct(id,name,year,rating,duration) ;

Q29.What do you mean by primitive Data type in Pig?

Int, Long, Float, Double, Char array, Byte array are the primitive data types in Pig.

Q30.What do you mean by a tuple in Pig?

An ordered set of the field of data is called Tuple.

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Top 10 Java Questions And Answers To Ace Your Microsoft Interview

Aiming for a career at Microsoft? Top 10 java questions and answers to ace your Microsoft interview

Aiming for a career at Microsoft is a worthy challenge given the company’s excellent track record for technological innovation, generous compensation packages, and flexible work-life balance. Java questions for Microsoft interviews are mostly about data structures, algorithms, Oops concepts, etc. The interviewer never asks questions related to Java only. The interviewer can also ask questions from Arrays, LinkedLists, Stacks, Queues, Strings, Patterns, and Binary trees, etc. Are you looking for Java questions and answers for Microsoft interviews? You are at the right place; this article features the top 10 java questions and answers to ace your Microsoft interview.

Top 10 java questions and answers for Microsoft interview:   How can we check whether the Binary Tree is BST or not?

In order to check whether the Binary Tree is BST or not, we simply check whether the left child node is smaller than the current node or not. We also check whether the right child node is greater than the current node or not. For a BST Binary tree, the left child should have smaller, and the right child should have greater value from its parent note. It is one of the crucial Java questions for Microsoft interview.

How to find an element in a sorted array of infinite size?

In order to find an element from an infinite sorted array, we set the index at position 100. If the element which we need to find is less from the index number, perform the binary search for the last 100 items. Else we set the index at position 200. We set the index by 100 until the item is greater. It is one of the top 10 java questions and answers to ace your Microsoft interview.

How can we reverse a linked list of size n?

Each element of a linked list stores the address of its next element or node. In order to reverse a linked list, we need to store the address of the previous node in each node of the linked list. We can reverse the linked list by using recursion or by using iteration. We use the following steps to reverse a linked list:

We first store the chúng tôi in a temporary pointer ptr

After that, we call the reverseLL(head.next) method

We store the pointer returned by the reverseLL(head.next) in a temporary variable temp

We set the next = head( ptr points to the last node of the reversed list in temp )

The temp variable points to the first node of the reversed linked list

Write an algorithm to build a tree from given Inorder and Preorder traversals.

We first take an element from the Preorder traversal and increment the index variable for picking the next element in the next recursive call.

From the picked element, we create a new tree node N.

Now, we get the index of that picked element from the given Inorder and store it in variable pos.

We call the constructTree() method for all the elements that are available before pos and create a tree as a left subtree of node N.

We call the constructTree() method for all the elements that are available after pos and create a tree as a right subtree of node N.

At last, we return node N

How can we determine whether a linked list contains a loop or cycle or not

We use two pointers, i.e., fast and slow at the time of iterating over the linked list. The slow and fast pointers move two and one nodes in each iteration, respectively. If the linked list contains a cycle, both pointers will meet at the same point during iteration. If both the pointers point to null, the linked list doesn’t contain any loop or cycle in it.

What is double-checked locking in Singleton?

Double-checked locking of Singleton is a way to ensure that only a single instance of a Singleton class is created through an application life cycle. The double-checked locking means that the code checks for an existing instance of Singleton class twice with and without locking to double ensure that no more than one instance of Singleton gets created. It is one of the top 10 java questions and answers to ace your Microsoft interview.

What are the scenarios in which we use the transient variable in Java?

A transient variable is a special type of variable in Java that is initialized during de-serialization with its default value. At the time of Serialization, the value of the transient variable is not serialized. In order to prevent any object from being serialized, we use the transient variable. We can easily create a transient variable by using the transient keyword.

Explain volatile in Java. Can we override the private method in Java?

We cannot override the private methods because we cannot access the private methods in the same way as non-private methods. The method overriding is possible in the child class only, and we cannot access the private methods in the child class. It is one of the crucial Java questions and answers for Microsoft interview.

Give the name of any two methods which we can override for an Object to be used as the key in HashMap.

Top 10 Awesome Agile Scrum Interview Questions And Answers For 2023

Introduction to Agile Scrum Interview Questions and Answers

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Now, if you are looking for a job that is related to Agile Scrum then you need to prepare for the 2023 Agile Scrum Interview Questions. It is true that every interview is different as per the different job profiles. Here, we have prepared the important Agile Scrum Interview Questions and Answers which will help you get success in your interview.

In this 2023 Agile Scrum Interview Questions article, we shall present 10 most important and frequently used Agile Scrum interview questions. These interview questions are divided into two parts are as follows:

Part 1 – Agile Scrum Interview Questions (Basic)

This first part covers basic Agile Scrum Interview Questions and Answers.

Q1. What is Scrum and how it can be used in Agile?

Agile is a methodology that can be implemented by using the Scrum framework for the purpose of the project management process. It contains a set of best engineering practices and best standards and also saves money, increases predictability, reduces failure, and improves the quality of the project being delivered. Scrum is a 20-year-old process that is successful in delivering the projects on time and quickly and as frequent deliverables instead of a single one.

Benefits to Vendors, Customers, Development teams, Project Managers, program managers, Product Managers, Management Executives, etc.,

It gives a high product or project quality.

More customer satisfaction.

Reduction in risks and increase in the project controllability.

Early starting of Development and faster returns on investment.

Focuses more on the business model and generates faster revenues.

Faster product releases and frequent deliverables.

Incremental Sprints can be delivered.

Quality can be easily maintained which is the key component of the scrum.

Transparency is established and every task can be easily tracked by the Product Owner and the Scrum master.

Data Analytics or overview board will be available depends on the tool being used to track or have a glance at the complete overview of the project in a single view.

Flexibility and Agility will be more in terms of delivering the project requirements and is also adaptable to the required changes.

Customer Satisfactory Index will also be more and the cost can also be controlled.

A product can easily be marketed in the shortest possible time.

Q4. What are the needs of Scrum?

The below is the list of few requirements of Scrum but are not exhausted :

It requires User Stories to describe the requirement and track the completion status of the assigned user story to the team member whereas Use Case is the older concept.

A name is required if it describes a sentence as a single line overview to give a simple explanation of the User Story.

A description is required as it gives a high-level explanation of the requirement to be met by the assignee.

Documents or attachments are also required so as to know about the story. For eg. In the case of any change in the User Interface Screen Layout, that can be easily known only by having a look at the Wire Frame or Prototype of the Screen model. This can be attached to the board using the attachment option.

Q5. What are the different roles available in Scrum?

The different roles present in the Scrum Agile process are Product Owner, Scrum Master, Team Members, etc., Product Owner is the one who keeps the requirements. The product owner also maintains the product backlog related things. The Product Owner will be the main interface between the project team, business, and customers of the entire project. Team members will be handling the day to day activities to fulfill the project requirements daily on the basis of the number of story points.

Part 2 – Agile Scrum Interview Questions (Advanced) Q6. How long does a Scrum Cycle last for?

A Scrum Cycle lasts for a minimum of 2 weeks to 4 weeks and a maximum of about a month. The Scrum Cycle includes the Product Owner, Team Member, and Scrum Master. A Scrum cycle also includes different Sprints as well. Each Sprint will be consisting of few Story points and each User Story will be assigned a few points.

Q7. What is the Product Backlog in Scrum?

Let us move to the next Agile Scrum Interview Questions.

Q8. What is the Scrum burndown chart?

The Scrum burndown chart contains two different coordinate axes called the X axis and the Y axis to monitor the working days or hours and the effort being assigned and tracked i.e. remaining effort out of the total effort. The x-axis indicates the number of working days and the Y-axis indicates the remaining effort to be fulfilled. It shows the real-time effort being carried out by the team members and in case of any delay or measures to be taken can be easily monitored and action is taken to avoid any kind of risk.

Q9. What is Velocity?

Velocity in a Sprint can be defined as the total amount of effort that a team is capable of fulfilling the project requirement. A velocity is defined as the number of story points divided by iteration.

Q10. What is an iteration in Scrum?

Iteration in Scrum is defined as the Complete Development Life Cycle in an Agile methodology of a Project. This term is commonly used in the Iterative and Incremental Development process.

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