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Coditas Interview Questions and Answers
Ques:- How do you ensure that Agile teams maintain focus and productivity during iterations
Right Answer:
* **Clear Sprint Goals:** Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for each iteration.
* **Daily Stand-ups:** Facilitate short, focused daily meetings to identify roadblocks and coordinate efforts.
* **Sprint Backlog Management:** Keep the sprint backlog refined, prioritized, and realistic based on team capacity.
* **Timeboxing:** Adhere to time limits for meetings and tasks to prevent scope creep and maintain momentum.
* **Focus on Value:** Prioritize tasks that deliver the most business value within the iteration.
* **Remove Impediments:** Proactively identify and resolve obstacles that hinder the team's progress.
* **Limit Work in Progress (WIP):** Encourage the team to focus on completing tasks before starting new ones.
* **Continuous Feedback:** Regularly review progress, gather feedback, and adapt plans as needed.
* **Defined "Definition of Done":** Ensure a clear understanding of what it means for a task to be considered complete.
* **Team Collaboration & Communication:** Foster open and effective communication and collaboration within the team.
Ques:- What tools or software do you use for Agile project management and why
Right Answer:
I've used tools like Jira, Azure DevOps, and Trello for Agile project management. I choose them based on project needs; Jira for complex workflows and robust reporting, Azure DevOps for integrated development environments, and Trello for simpler, visually-oriented task management.
Ques:- How do you prioritize features or tasks in an Agile sprint
Right Answer:
We prioritize features or tasks in an Agile sprint using a combination of factors like business value, risk, effort/size, dependencies, and urgency. Product Owner usually leads this, using techniques like MoSCoW (Must have, Should have, Could have, Won't have) or story pointing, to ensure the most valuable items are tackled first.
Ques:- What is Agile methodology, and how does it differ from traditional project management approaches
Right Answer:
Agile is an iterative and incremental approach to project management that focuses on collaboration, flexibility, and customer satisfaction. Unlike traditional, sequential (waterfall) methods, Agile embraces change throughout the project lifecycle through short development cycles called sprints.
Ques:- Can you describe a time when an Agile project didn’t go as planned and how you handled it
Right Answer:
"In one project, we underestimated the complexity of integrating a new third-party API. This caused us to miss our sprint goal. To address this, we immediately re-estimated the remaining work, broke down the integration into smaller, more manageable tasks, and increased communication with the API vendor. We also temporarily shifted team focus to prioritize the integration, delaying a lower-priority feature for the next sprint. Finally, in the sprint retrospective, we implemented a better vetting process for third-party integrations to avoid similar issues in the future."
Ques:- What is CORS and how does it affect API development
Right Answer:
CORS, or Cross-Origin Resource Sharing, is a security feature implemented by web browsers that allows or restricts web applications from making requests to a domain different from the one that served the web page. It affects API development by requiring developers to configure their APIs to specify which origins are allowed to access their resources, ensuring that only trusted domains can interact with the API.
Ques:- What are Webhooks and how do they differ from APIs
Right Answer:
Webhooks are user-defined HTTP callbacks that are triggered by specific events in a web application, allowing real-time data transfer. They differ from APIs in that APIs require a request to be made to receive data, while webhooks automatically send data when an event occurs without needing a request.
Ques:- What is OAuth and how does it work in API authentication
Right Answer:
OAuth is an open standard for access delegation commonly used for token-based authentication and authorization. It allows third-party applications to access a user's resources without sharing their credentials.

In API authentication, OAuth works by having the user authorize the application to access their data. The process involves:

1. The user is redirected to an authorization server to log in and grant permission.
2. The authorization server issues an access token to the application.
3. The application uses this access token to make API requests on behalf of the user.
4. The API validates the token and grants access to the requested resources.
Ques:- What is API authentication and what are common methods
Right Answer:
API authentication is the process of verifying the identity of a user or application trying to access an API. Common methods include:

1. **API Keys**: Unique keys provided to users to access the API.
2. **Basic Authentication**: Uses a username and password encoded in Base64.
3. **OAuth**: A token-based authentication method that allows users to grant limited access to their resources without sharing credentials.
4. **JWT (JSON Web Tokens)**: A compact, URL-safe means of representing claims to be transferred between two parties, often used for stateless authentication.
5. **HMAC (Hash-based Message Authentication Code)**: Uses a secret key to create a hash of the request, ensuring data integrity and authenticity.
Ques:- What is the difference between REST and SOAP APIs
Right Answer:
REST (Representational State Transfer) is an architectural style that uses standard HTTP methods and is typically more lightweight and easier to use, while SOAP (Simple Object Access Protocol) is a protocol that relies on XML for message format and has strict standards for security and transactions. REST is generally more flexible and faster, while SOAP is more suited for enterprise-level services requiring high security and reliability.
Ques:- What is data normalization and why is it important
Right Answer:
Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves structuring the data into tables and defining relationships between them. Normalization is important because it helps eliminate duplicate data, ensures data consistency, and makes it easier to maintain and update the database.
Ques:- What is a hypothesis and how do you test it
Right Answer:
A hypothesis is a specific, testable prediction about the relationship between two or more variables. To test a hypothesis, you can use the following steps:

1. **Formulate the Hypothesis**: Clearly define the null hypothesis (no effect or relationship) and the alternative hypothesis (there is an effect or relationship).
2. **Collect Data**: Gather relevant data through experiments, surveys, or observational studies.
3. **Analyze Data**: Use statistical methods to analyze the data and determine if there is enough evidence to reject the null hypothesis.
4. **Draw Conclusions**: Based on the analysis, conclude whether the hypothesis is supported or not, and report the findings.
Ques:- What is regression analysis and when is it used
Right Answer:
Regression analysis is a statistical method used to examine the relationship between one dependent variable and one or more independent variables. It is used to predict outcomes, identify trends, and understand the strength of relationships in data.
Ques:- What are outliers and how do you handle them in data analysis
Right Answer:
Outliers are data points that significantly differ from the rest of the dataset. They can skew results and affect statistical analyses. To handle outliers, you can:

1. Identify them using methods like the IQR (Interquartile Range) or Z-scores.
2. Remove them if they are errors or irrelevant.
3. Transform them using techniques like log transformation.
4. Use robust statistical methods that are less affected by outliers.
5. Analyze them separately if they provide valuable insights.
Ques:- How do you handle missing data in a dataset
Right Answer:
To handle missing data in a dataset, you can use the following methods:

1. **Remove Rows/Columns**: Delete rows or columns with missing values if they are not significant.
2. **Imputation**: Fill in missing values using techniques like mean, median, mode, or more advanced methods like KNN or regression.
3. **Flagging**: Create a new column to indicate missing values for analysis.
4. **Predictive Modeling**: Use algorithms to predict and fill in missing values based on other data.
5. **Leave as Is**: In some cases, you may choose to leave missing values if they are meaningful for analysis.
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