American Broadcasting Company Data Scientist Interview Questions and Answers

DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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Q1. 1. First question was, from my project asked me that explain one project.
Ans. I have done three project, out of this three project one project was movie recommendation syatem. That i was explained. Then some counter question from project like how did you convert text to numerical, which technique you used here. I able to give all questions answer from project.
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Q2. 2. After they asked me, what is your subject preference?
Ans. My subject preference was NLP, Deep Learning, ML. Afrer that asked some question from NLP like what is term document matrix, tf-idf, word embedding, Gradient descent, why we have to change learning rate after every epoch, how lr change, precision recall.
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Q3. 3. My one projcet was sentimental analysis that was by LSTM model. So, next question was explain how LSTM model work and architecture .
Ans. Then I was explained workin flow of LST model and architecture. Then asked me did you know bidireectional LSTM. I said yes, then explained.
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Q4. 4. One question was from sql. what is the condition for primary key? last question one case business case study. this is all about my interview. My interview was 50 min and selected for HR round.
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Q1. Phone call round, just asked me are you interested to come here.
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Q1. Write a SQL query to find all duplicate emails in a table named person
Ans. 
SQL query to find duplicate emails in a table named person
• Use GROUP BY and HAVING clause to group emails and count their occurrences

• Select only those emails which have count greater than 1

• Example: SELECT email, COUNT(*) FROM person GROUP BY email HAVING COUNT(*) > 1;
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Q2. Given a weather table, write a sql query to find all date's ids with higher temperature compared to it's previous dates
Ans. 
SQL query to find date ids with higher temperature compared to previous dates in weather table
• Use self join to compare temperature of current date with previous dates

• Order the table by date to ensure correct comparison

• Select date ids where temperature is higher than previous dates
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The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.

Normal aptitude questions

2hrs - sections include aptitude, machine learning, deep learning and two easy python coding questions

Q1. Resume scrutiny with projects and internships
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Q2. How to measure 45 mins duration using two identical wires
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Q3. Partition a round cake into eight equal parts within three cuts
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Q4. 10 coins puzzle (5 heads up and 5 tails up)
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