Clarivate Interview Questions, Process, and Tips

Written exam on our subject questions

Q1. What is treadmark
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Treadmark is a term used to describe the wear and tear on a tire's surface caused by friction with the road.
• Treadmark is the pattern of grooves and ridges on the surface of a tire that provides traction on the road.

• It is important to regularly check the tread depth of tires to ensure they are safe to drive on.

• Tread wear can also be an indicator of other issues with a vehicle's alignment or suspension.

• Examples of tread ...read more
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Q2. What is patent and why patent is filled and which are major countries
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Patent is a legal right granted to an inventor to exclude others from making, using, or selling their invention.
• Patent is filed to protect an invention from being copied or used without permission.

• It gives the inventor exclusive rights to make, use, and sell their invention for a certain period of time.

• Patent laws vary by country, but major countries for patent filings include the US, Japan, China, and European countri...read more
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Q1. Mostly from your resume and best practices of Salesforce.
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Q2. Thread and unthread in Email-to-case
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Q3. Trigger best practices
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Q4. Code bulkification
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Q5. Flow structures
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Q1. Read a comprehensive paragraph regarding a drug and answer few questions;
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Q2. Know details regarding US healthcare utilisation management
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Q1. Can you explain your projects?
Ans. 
I have worked on various projects involving data analysis, visualization, and interpretation.
• Developed predictive models using machine learning algorithms

• Performed data cleaning and preprocessing to improve data quality

• Created interactive dashboards for data visualization

• Conducted statistical analysis to identify trends and patterns

• Collaborated with cross-functional teams to derive actionable insights
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Q2. What tuning parameters do you typically utilize in your work?
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I typically utilize tuning parameters such as learning rate, regularization strength, batch size, and number of epochs.
• Learning rate: Adjusting the step size during optimization to control how quickly the model learns.

• Regularization strength: Balancing between fitting the training data well and preventing overfitting.

• Batch size: Determining the number of samples processed before updating the model.

• Number of epochs: Set...read more
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