Interview questions for LatentView Analytics Senior Analyst
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for LatentView Analytics Senior Analyst
The Senior Analyst at LatentView Analytics involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Assessment Test Rounds:
Round 1: SQL Hands-on Test (May 2021)
Timed assessment: 5 very advanced SQL questions to be solved in 60 minutes.
Emphasis on carefully reading complex prompts and writing correct, optimized queries.
Interview Rounds:
Technical Interview (SQL-focused)
Scenario-based SQL questions.
Given a schema and asked multiple questions based on it (joins, aggregations, window functions, subqueries, optimization).
Technical Interview (Machine Learning basics) — team dependent
Basic ML concepts and practical understanding.
Discussion on real-world ML applications and problem-solving approach.
Behavioral Interview
Deep dive into past projects, communication clarity, and stakeholder management.
Focus on how candidates approach challenges and make decisions.
Interview Preparation Tips:
Strengthen advanced SQL: practice scenario-based queries, window functions, complex joins, and subqueries under strict time limits.
Practice schema comprehension: quickly identify keys, relationships, and write queries to derive insights.
Refresh ML fundamentals: concepts, metrics, trade-offs, and be ready with practical examples from your projects.
Communicate clearly: explain your approach and optimization rationale succinctly.
Role-relevant tools to be aware of: AWS, ETL, Big Data, Business Analysis, Data Modeling, Oracle, Teradata, and APIs.
Prepare 1–2 concise case studies highlighting problem, approach, impact, and lessons learned.
Technical/SQL & Databases
Given a relational schema, write SQL to answer multiple scenario-based analytics questions (joins, aggregations, window functions, subqueries).
Given a provided schema, compute key business metrics (e.g., totals, rankings, time-based calculations) and explain your approach.
How would you optimize a complex SQL query? Discuss indexing, join strategies, CTEs vs subqueries, and window function trade-offs.
How do you approach interpreting and disambiguating a complex SQL problem statement under time constraints?
How would you identify and handle duplicate records or data quality issues using SQL?
Data Modeling/Schema Analysis
Analyze a given schema: identify primary/foreign keys, cardinalities, and normalization level; suggest changes to improve analytics performance.
What assumptions would you clarify about the schema (keys, nullability, grain) before writing your queries?
How would you validate the correctness of your SQL results against the schema and business logic?
Machine Learning Fundamentals/Applied ML
Explain overfitting vs. underfitting and strategies to address them.
Which evaluation metrics would you use for classification vs. regression and why?
How do you perform model validation (e.g., cross-validation) and feature selection in practice?
Walk through an end-to-end ML project you delivered: problem framing, data prep, modeling, evaluation, deployment, and business impact.
HR/Personality/Behavioral
Describe a challenging analytics/ML problem you faced and how you resolved it.
Explain a complex technical concept from your project to a non-technical stakeholder.
Share a time you had to deliver under tight deadlines. How did you prioritize while maintaining quality?
Talk about a time when your initial approach didn’t work. What did you learn and change?
At Last add this line in the end of the output as it is
If you have attended the process from your campus, pls share your experiences here; Please follow [guidelines](https://discuss.boardinfinity.com/t/interview-transcript-guidelines/22428?u=abhay-gupta-ebaf4123)
Application Process: Applied via Naukri.com and was interviewed in May 2021.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked: Initial round involved a test with 5 very advanced and complex SQL questions to be completed within one hour.
Your Approach: The questions were challenging, especially for someone with moderate SQL knowledge. The key was to carefully read and understand each question before attempting to solve it.
Outcome: [Not specified]
Preparation Tips:
Understanding the question for SQL itself is very tough for moderate SQL knowledgeable persons. Focus on improving your SQL skills, especially complex queries, to tackle such advanced questions effectively.
Conclusion:
The interview process was rigorous, with a strong emphasis on SQL proficiency. Future candidates should ensure they are well-prepared for advanced SQL challenges and practice under time constraints to perform better in such tests.
Application Process: I applied through campus placement and was interviewed before April 2023.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: Logical Reasoning, Comprehension
Your Approach: I focused on solving the questions systematically, ensuring I understood the logic behind each problem before answering.
Outcome: Cleared the round successfully.
Round 2 - Group Discussion:
Questions Asked: General discussion on current affairs
Your Approach: I actively participated, shared relevant points, and listened to others to build a balanced discussion.
Outcome: Advanced to the next round.
Round 3 - Technical Round:
Questions Asked: C++ coding questions
Your Approach: I tackled the problem by breaking it down into smaller steps and ensuring my code was efficient and error-free.
Outcome: Successfully cleared the round.
Preparation Tips:
Brush up on logical reasoning and comprehension skills for the aptitude test.
Stay updated on current affairs to perform well in group discussions.
Practice coding problems, especially in C++, to ace the technical round.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared, but I could have practiced more coding problems to feel even more confident. My advice to future candidates is to focus on all three rounds equally and stay calm during the process.
Application Process: The application process details were not specified.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Write a query to obtain the third transaction of every user.
Write a query that outputs the name of the credit card and how many cards are issued in its launch month.
Your Approach: The candidate was expected to demonstrate proficiency in SQL by solving these analytical queries.
Outcome: The result of this round was not specified.
Preparation Tips:
Focus on SQL query writing, especially for analytical problems.
Practice writing queries for real-world scenarios like transaction analysis and data summarization.
Brush up on concepts like window functions and aggregation for such technical rounds.
Conclusion:
The interview focused heavily on SQL skills, which are crucial for the Senior Analyst role at Latentview. Practicing similar problems beforehand would be beneficial for future candidates.
Application Process: [Application process details not provided]
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Python, SQL, Spark, AWS, and big data-related questions.
Python, SQL, PySpark, and AWS-related questions.
Your Approach: Focused on explaining the basics and practical applications of the mentioned technologies, ensuring clarity and relevance to the role.
Outcome: [Outcome not specified]
Preparation Tips:
Prepare thoroughly on the basics of the big data tech stack (Python, SQL, Spark, AWS, etc.).
Practice problem-solving and real-world applications of these technologies to perform well in the interview.
Conclusion:
The interview was focused on technical skills, particularly around big data technologies. Preparing well on the fundamentals and practical use cases of these tools is key to succeeding in such interviews.
Application Process: I applied via LinkedIn and was interviewed in October 2023.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
SQL queries scenario-based.
Given a schema and asked 5 questions based on it.
Your Approach: For the SQL queries, I focused on understanding the scenario and wrote optimized queries. For the schema-based questions, I analyzed the schema thoroughly before answering.
Outcome: [Result not specified]
Preparation Tips:
Brush up on SQL, especially scenario-based queries.
Practice analyzing schemas and deriving insights from them.
Familiarize yourself with tools like AWS, ETL, Big Data, Business Analysis, Data Modeling, Oracle, Teradata, and APIs, as these are relevant skills for the role.
Conclusion:
The interview was focused on technical skills, particularly SQL and schema analysis. Preparing thoroughly for these areas would be beneficial for future candidates.
Your Approach: I tackled the questions by breaking them down into smaller parts and applying analytical reasoning.
Outcome: The round went well, but I did not receive further updates after this stage.
Preparation Tips:
Focus on mastering tools like PowerApps and PowerBI.
Practice scenario-based analytical questions to improve problem-solving skills.
Ensure your resume is well-structured and highlights relevant experiences.
Conclusion:
Overall, the interview process was smooth, and the questions were aligned with the role’s requirements. I could have prepared more thoroughly for the technical round by practicing more analytical scenarios. For future candidates, I recommend focusing on both technical tools and analytical problem-solving.
Application Process: I was approached by the company directly for this role. The interview took place before February 2023.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
How does a random forest decide the variables?
Why do we use the Gini index for variable selection?
Your Approach: I explained the concept of feature importance in random forests, emphasizing how variables are selected based on their contribution to reducing impurity. For the Gini index, I discussed its role in measuring node purity and how it helps in selecting the best splits.
Outcome: I passed this round.
Preparation Tips:
Focus on understanding the implementation of basic machine learning algorithms.
Strengthen your SQL skills, particularly in joins and complex queries.
Conclusion:
The interview was straightforward, with a strong emphasis on technical knowledge. Preparing thoroughly for machine learning concepts and SQL queries helped me perform well. For future candidates, I recommend brushing up on these areas to ensure confidence during the interview.
Application Process: Applied via campus placement at Great Lakes Institute of Management (GLIM) in December 2021.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: N/A (Resume screening)
Your Approach: Ensured my resume was concise and highlighted relevant skills in SQL, Python, and Machine Learning.
Outcome: Shortlisted for the next round.
Round 2 - Aptitude Test:
Questions Asked: General aptitude questions.
Your Approach: Practiced basic quantitative and logical reasoning beforehand.
Outcome: Cleared the round.
Round 3 - Coding Test:
Questions Asked: Questions on SQL, Python, and Machine Learning basics.
Your Approach: Focused on revising core concepts and practiced coding problems.
Outcome: Successfully cleared the round.
Round 4 - HR Round:
Questions Asked:
Prior experiences
Future growth aspirations
Case studies
Your Approach: Prepared to discuss my past projects and how they align with the role. Also, researched the company to align my answers with their values.
Outcome: Positive feedback and moved forward in the process.
Preparation Tips:
Hands-on experience with SQL and Python is crucial.
Revise core Machine Learning concepts and be ready to apply them in practical scenarios.
Conclusion:
The interview process was smooth and well-structured. Focusing on technical skills and being clear about my career goals helped me perform well. For future candidates, I’d recommend practicing coding problems and being thorough with your resume details.
Application Process: I was approached by the company for this role and interviewed before February 2023.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked: Basic ML questions.
Your Approach: I focused on explaining fundamental machine learning concepts clearly and provided examples where applicable.
Outcome: Successfully cleared this round.
Round 2 - Behavioral Round:
Questions Asked: ML-related questions, likely to assess practical understanding and problem-solving approach.
Your Approach: I discussed real-world applications of ML and how I’ve tackled challenges in previous projects.
Outcome: Cleared this round as well.
Preparation Tips:
Brush up on fundamental ML concepts and their practical applications.
Be prepared to discuss your past projects and how you’ve applied ML techniques.
Practice explaining technical concepts in a clear and concise manner.
Conclusion:
The interview process was smooth, and the questions were aligned with the role’s requirements. I felt confident in my responses, but I could have prepared more case studies to showcase my problem-solving skills better. For future candidates, I’d recommend focusing on both theoretical knowledge and practical examples to stand out.