Akira Consultancy Data Science Consultant Interview Questions and Answers

30 minutes. 3 aptitude questions . Very easy .

DSA Questions. Palindrome , Arrays and Strings were the most asked questions in my batch.

Q1. What are directors?
Ans. 
Directors are individuals responsible for overseeing the activities and operations of a company or organization.
• Directors are typically appointed by shareholders or members of the organization.

• They are responsible for making strategic decisions and setting goals for the organization.

• Directors have a fiduciary duty to act in the best interests of the company and its stakeholders.

• Examples of directors include the CEO, CF
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Q2. OOP COncepts such as polymorphism, inheritance, etc.
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NER training using deep learning

Q1. Describe the approach taken for assignment
Ans. 
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
• Break down the assignment into smaller tasks to make it more manageable

• Set deadlines for each task to stay on track

• Regularly check progress to ensure everything is on schedule

• Seek feedback from colleagues or supervisors to improve the quality of work
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Q2. Scenario based questions
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Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Q1. Asked questions related to stats , Senario based. Interviewer was very nice
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Q2. Asked questions on Python Programming and asked for Programs Like How can update List , Asked to Build a Tough Dict , Class etc.
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Q1. Why u want to join?
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Q2. What is your future goal?
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Q1. Basic questions in python
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Q2. Questions in Ml
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Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Q1. Questions on Python datatypes, class, object, inheritance,
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Q2. Machine learning algorithms
Ans. 
Machine learning algorithms are used to train models on data to make predictions or decisions.
• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve learning through trial and error.

• Examples of machine learning applications include image recognition, natural l
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Q3. Sql queries and python coding questions
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Q1. Related to project
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