Deloitte Senior Consultant Interview Questions, Process, and Tips

Q1. Explain bias variance in Ml
Ans. 
Bias-variance tradeoff is a key concept in machine learning, balancing model complexity and generalization.
• Bias refers to error from overly simplistic assumptions in the model, leading to underfitting.

• Variance refers to error from model being too complex and fitting to noise in the training data, leading to overfitting.

• Balancing bias and variance is crucial for optimal model performance and generalization.

• Regularizatio...read more
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Q2. End to end deployment over cloud
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End to end deployment over cloud involves deploying an application or system from start to finish using cloud services.
• Plan the deployment process including resource allocation and configuration

• Use automation tools like Ansible or Terraform for consistent deployment

• Monitor the deployment for any issues and scale resources as needed

• Implement security measures to protect data and applications

• Consider factors like cost, s...read more
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Q3. Monitoring ML model in production
Ans. 
Monitoring ML model in production involves tracking model performance, data drift, and making necessary adjustments.
• Set up monitoring tools to track model performance metrics such as accuracy, precision, recall, and F1 score.

• Monitor data drift by comparing the distribution of incoming data with the training data.

• Implement alerting systems to notify when model performance drops below a certain threshold.

• Regularly retrai...read more
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Q4. Hyper parameter tuning
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Q5. Azure machine learning
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Q6. Databricks jobs and pyspark internals
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Q1. What is mlops and how it is different from machine learning
Ans. 
MLOps is the practice of combining machine learning with operations to deploy, monitor, and manage ML models in production.
• MLOps focuses on the entire ML lifecycle from development to deployment and monitoring

• It involves automating the process of training, deploying, and managing ML models

• MLOps ensures collaboration between data scientists and operations teams for seamless model deployment

• It includes version control, t...read more
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Q2. How do you monitor models in production
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Models in production are monitored through automated alerts, performance metrics, and regular reviews.
• Set up automated alerts to notify of any anomalies or errors in the model's predictions

• Track performance metrics such as accuracy, precision, recall, and F1 score regularly

• Conduct regular reviews of the model's outputs and compare them to expected results

• Implement version control to track changes and updates to the mod
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Q3. What are the challenges of deploying machine learning model in production
Ans. 
Challenges include data quality, model interpretability, scalability, and monitoring.
• Data quality issues can lead to biased or inaccurate predictions.

• Interpretability of complex models can be a challenge for stakeholders.

• Scalability concerns arise when deploying models to handle large volumes of data.

• Monitoring the model's performance in real-time is crucial to ensure its effectiveness over time.
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Q4. How do you reduce latency
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Reducing latency involves optimizing network performance and minimizing delays in data transmission.
• Optimize network infrastructure by upgrading hardware and software

• Use content delivery networks (CDNs) to cache data closer to end users

• Implement data compression techniques to reduce file sizes and transmission times

• Utilize load balancing to distribute network traffic evenly across servers

• Minimize unnecessary network ho
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Q1. 2 SQL Questions medium level, Spark, Databricks, ETL questions - basics level
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Q2. Python programming question - 1 easy question
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Q1. Spark Internals, Azure data related services, Project discussions
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Q2. Data platform basic questions
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Q1. Salary expectations
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Q2. Salary budget and explanation
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Q1. Lots of detailed oriented technical questions on Alteryx and PowerBI
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Data transformation using Alteryx

Q1. General HR related topics
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Q1. Data base query optimization
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Q2. Validations in asp net mvc
Ans. 
Validations in ASP.NET MVC
• ASP.NET MVC provides various validation techniques such as Data Annotations, Model Validation, and Client-Side Validation

• Data Annotations are attributes that can be applied to model properties to specify validation rules

• Model Validation is performed on the server-side and can be customized using validation attributes and custom validation logic

• Client-Side Validation is performed on the client-...read more
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Q1. Exception handling in web api
Ans. 
Exception handling in web api
• Use try-catch blocks to handle exceptions

• Return appropriate HTTP status codes for different types of exceptions

• Log exceptions for debugging purposes

• Use global exception handling middleware to handle unhandled exceptions

• Provide meaningful error messages to the client
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Q2. Cors in web api c#
Ans. 
CORS is a security feature in web API to restrict cross-origin requests.
• CORS stands for Cross-Origin Resource Sharing.

• It is used to restrict cross-origin requests made by web browsers.

• It is implemented in web API using middleware.

• It can be configured to allow specific origins, headers, and methods.

• Example: app.UseCors(options => options.AllowAnyOrigin().AllowAnyHeader().AllowAnyMethod());
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Q1. Thorough through your job profile.
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Q2. Be very careful about details
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Q1. Tell me about yourself
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Financial services, automobile and hospitality