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Nasa Associate Scientist Interview Questions and Answers
Nasa Associate Scientist Interview Experiences
1 interview found
Anonymous
Selected
I applied via Referral and was interviewed before Nov 2023. There were 2 interview rounds.
(1 Question)
- Q1. What kind of work culture do you like?
(1 Question)
- Q1. Explain the bias-variance tradeoff
- Ans.
Bias-variance tradeoff refers to the balance between underfitting and overfitting in machine learning models.
Bias refers to the error introduced by approximating a real-world problem, while variance refers to the error introduced by the model's sensitivity to fluctuations in the training data.
High bias can lead to underfitting, where the model is too simple to capture the underlying patterns in the data.
High variance c...
Skills evaluated in this interview
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I applied via Company Website and was interviewed in Oct 2024. There was 1 interview round.
(3 Questions)
- Q1. Difference between Data Warehouse, Data Lakes and Tables
- Ans.
Data Warehouse stores structured data for reporting and analysis, Data Lakes store raw and unstructured data, Tables are basic data structures.
Data Warehouse is used for storing structured data from various sources for reporting and analysis.
Data Lakes store raw and unstructured data in its native format for future processing and analysis.
Tables are basic data structures used to organize and store data in a structured ...
- Q2. Query difference betweeen Group by and Partition by
- Ans.
Group by is used to group rows that have the same values into summary rows, while Partition by is used to divide the result set into partitions to which the function is applied separately.
Group by is used with aggregate functions to group rows based on a column or set of columns.
Partition by is used with window functions to divide the result set into partitions.
Group by is used with SELECT statement, while Partition by...
- Q3. Machine Learning questions such as Regression, Trees etc
Interview Preparation Tips
- SQL
Skills evaluated in this interview
(1 Question)
- Q1. Basic ML questions were asked.
Selected
I applied via Referral and was interviewed before Jan 2023. There were 3 interview rounds.
Python and SQL coding test along with basic data science questions
Python was on basic syntax. SQL was on medium difficulty
(1 Question)
- Q1. Correlation vs causation Central limit theorem Support Vector Machines Confusion matrix Basics of Deep Learning - activation function, optimizers and initialisation techniques Z score, P value Differenc...
(1 Question)
- Q1. Questions about my background My hobbies and interests
Interview Preparation Tips
They expect you to know broad areas even though it might be of shallow depth
Basic aptitude ques were asked
(2 Questions)
- Q1. Projects discussion
- Q2. Probability questions
(2 Questions)
- Q1. Why Ericsson and 5 year plan
- Q2. Your strength and weakness
rakesh kumar Sharma
(2 Questions)
- Q1. ML questions and deep learning
- Q2. Project in your cv
I applied via Company Website and was interviewed in Feb 2024. There were 2 interview rounds.
(1 Question)
- Q1. Isolatn forest work? evalution metrics in laymann tems , pyspark basics , job lib
(1 Question)
- Q1. Scenerio based data science questns , paramatric and non parametric data
I was interviewed in Feb 2024.
(1 Question)
- Q1. Statistics, Machine Learning, Project -Easy to moderate questions
Interview Preparation Tips
prakhar pandey
(1 Question)
- Q1. Statistics, python, machine learning, regression
(1 Question)
- Q1. Statistics, machine learning, regression, python
(1 Question)
- Q1. Tell me about yourself
Selected
I applied via LinkedIn and was interviewed before May 2023. There were 2 interview rounds.
(1 Question)
- Q1. It was around recent projects, did an entire walkthrough of one of my projects related to deduplication of nlp content. At some of the stages, alternative objectives were evaluated. Ex: How to handle incre...
(1 Question)
- Q1. This was more on the depth of ML concepts. Information loss vs dimensional reductions, transformer architecture, metrics for different problem settings, loss functions, outlier analysis, drift detection et...
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