Blackstraw AI Interview Questions, Process, and Tips

Q1. All about the technical knowledge of powerbi
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Q2. Cardinality explanation?
Ans. 
Cardinality refers to the uniqueness of values in a column or set of columns in a database table.
• Cardinality is the number of unique values in a column or set of columns.

• High cardinality means a column has many unique values, while low cardinality means few unique values.

• For example, a column like 'employee_id' would have high cardinality, while a column like 'gender' would have low cardinality.
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Q3. Transformationprocess
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Q1. About our personality questions
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Q1. How do you choose an ML algorithm basis the data given
Ans. 
ML algorithm selection is based on data characteristics, problem type, and desired outcomes.
• Understand the problem type (classification, regression, clustering, etc.)

• Consider the size and quality of the data

• Evaluate the complexity of the model and interpretability requirements

• Choose algorithms based on their strengths and weaknesses for the specific task

• Experiment with multiple algorithms and compare their performance

• F...read more
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Q1. How do u optimise a ML model How good are you in coding with Python. Rate yourself
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To optimize a ML model, one can tune hyperparameters, feature engineering, cross-validation, ensemble methods, and regularization techniques.
• Tune hyperparameters using techniques like grid search or random search

• Perform feature engineering to create new features or select relevant features

• Utilize cross-validation to evaluate model performance and prevent overfitting

• Explore ensemble methods like bagging and boosting to ...read more
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Basic coding test with latest java tech stack

Q1. Java, java8, spring, springboot and microservices
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Q1. Challenges you faced and how you overcame
Ans. 
Overcoming challenges as a Senior Program Manager
• Managing conflicting priorities and tight deadlines

• Building consensus among cross-functional teams

• Adapting to changing project requirements

• Resolving conflicts and mitigating risks

• Implementing effective communication strategies
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Q2. How you manage Risks
Ans. 
I manage risks by identifying, assessing, prioritizing, and mitigating them.
• I identify risks by analyzing project requirements, stakeholder expectations, and potential obstacles.

• I assess risks by evaluating their likelihood and impact on project objectives.

• I prioritize risks by ranking them based on their severity and potential consequences.

• I mitigate risks by developing and implementing risk response plans, such as co...read more
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Q1. About projects and explain ML or DL algorithms.
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Q2. More questions about latest technologies and deep questions about algorithms.
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Q1. Projects and ML DL.
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Q2. Few things from deployment.
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Q1. Salary discussion and joining date confirmation.
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Q1. 1. difference between list & tuple 2. describe your day-to-day work 3. describe your favourite project
Ans. 
List is mutable, tuple is immutable. Day-to-day work involves data analysis and modeling. Favorite project involved developing a predictive analytics model.
• List can be modified after creation, tuple cannot

• List uses square brackets [], tuple uses parentheses ()

• Day-to-day work includes data cleaning, exploratory data analysis, model building, and communication of results

• Favorite project involved collecting and analyzing ...read more
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Q2. How does your day look like
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Q3. Your favourite project
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Q4. Difference between regression & classification based algorithms
Ans. 
Regression predicts continuous values, while classification predicts discrete values.
• Regression algorithms predict continuous values, such as predicting house prices based on features like size and location.

• Classification algorithms predict discrete values, such as classifying emails as spam or not spam based on content.

• Regression algorithms include linear regression, polynomial regression, and support vector regressio...read more
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Q5. Difference between RNN & CNN
Ans. 
RNN is used for sequential data like time series, while CNN is used for spatial data like images.
• RNN processes sequential data by maintaining memory of past inputs, suitable for time series forecasting.

• CNN is designed for spatial data like images, using filters to extract features and patterns.

• RNN is good for text data analysis, language translation, and speech recognition.

• CNN is commonly used in image recognition, obj
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