Associate Project Manager Interview Questions and Answers for Freshers

APM stands for Associate Project Manager.

• APM is a role in project management that involves assisting project managers in planning, organizing, and executing projects.

• APMs are responsible for coordinating project activities, monitoring progress, and ensuring timely completion.

• They may also assist in budgeting, resource allocation, and risk management.

• APMs often work closely with cross-functional teams and stakeholders to ensure project success.

• Examples of APM tasks include cre...read more

In waterfall model, the testing methodology used is mostly traditional and sequential.

• Testing is typically done at the end of the development cycle.

• Each phase must be completed before the next phase begins.

• Testing is done after the development phase is completed.

• There is no overlapping of phases in waterfall model.

• Examples of testing methodologies used in waterfall model include manual testing, system testing, and user acceptance testing.

The machine learning model used on our website is a recommendation system based on collaborative filtering.

• The website uses collaborative filtering to recommend products or content to users based on their past interactions and similarities with other users.

• Collaborative filtering is a type of recommendation system that makes automatic predictions about the interests of a user by collecting preferences from many users.

• Examples of collaborative filtering models include user-bas...read more

Some drawbacks of Jira include complexity for beginners, high cost for large teams, and limited customization options.

• Complexity for beginners in terms of learning curve and navigation

• High cost for large teams or organizations

• Limited customization options compared to other project management tools

• Dependency on internet connectivity for cloud-based version

• Potential for information overload with excessive notifications and updates

Supervised learning is a type of machine learning where the model is trained on labeled data to make predictions or decisions.

• In supervised learning, the algorithm learns from labeled training data, where each example is a pair consisting of an input object and a desired output value.

• The goal is to learn a mapping from inputs to outputs, so that the model can make predictions on new, unseen data.

• Examples of supervised learning algorithms include linear regression, support vec...read more