As one of the leading companies in the oil and gas industry, Schlumberger Ltd understands the importance of data science and analytics in driving informed decision-making and optimizing operations. For candidates aspiring to join Schlumberger’s data science and analytics teams, preparing for the interview process is crucial. In this blog, we’ll explore some common interview questions along with their answers to help candidates ace their interviews at Schlumberger.
Table of Contents
Technical Interview Questions
Question: What is the difference between an inner join and an outer join?
Answer: In an inner join, only the rows with matching values in both tables are included in the result set. Conversely, in an outer join, all rows from at least one of the tables are included, regardless of whether there are matching values in the other table.
Question: What is a logistic regression classifier?
Answer: Logistic regression is a statistical method used for binary classification tasks, where the goal is to predict the probability that an instance belongs to a particular class. It models the relationship between a binary dependent variable and one or more independent variables by estimating probabilities using the logistic function.
Question: What is the output of a CNN after applying a filter?
Answer: The output of a Convolutional Neural Network (CNN) after applying a filter is a feature map. This feature map represents the presence of specific features or patterns in the input data that the filter is designed to detect.
Question: What’s the difference between supervised and unsupervised machine learning?
Answer: Supervised machine learning involves training a model on labeled data, where the desired output is provided, allowing the model to learn the relationship between input features and the target variable. Unsupervised machine learning, on the other hand, deals with unlabeled data, where the model learns patterns and structures in the data without explicit guidance on the desired output.
NLP Interview Questions
Question: What is NLP?
Answer: Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. It enables computers to understand, interpret, and generate human language.
Question: What are some common NLP tasks?
Answer: Common NLP tasks include text classification, named entity recognition (NER), sentiment analysis, machine translation, summarization, and question answering.
Question: Explain the term “tokenization” in NLP.
Answer: Tokenization is the process of breaking text into smaller units called tokens, which can be words, characters, or subwords. It is a fundamental preprocessing step in NLP tasks.
Question: What is stemming and lemmatization?
Answer: Stemming is the process of reducing words to their root or base form by removing affixes. Lemmatization, on the other hand, involves reducing words to their canonical form (lemma) based on their dictionary definitions.
Question: How does a neural network-based language model work?
Answer: A neural network-based language model learns to predict the probability of a word given its context in a sequence of words. It typically consists of an input layer, one or more hidden layers, and an output layer. The model is trained on a large corpus of text data using techniques like backpropagation and gradient descent.
Question: What is the difference between word embeddings and one-hot encoding?
Answer: One-hot encoding represents each word in a vocabulary as a binary vector with a single “1” indicating the presence of the word. Word embeddings, on the other hand, represent words as dense vectors in a continuous vector space, capturing semantic relationships between words.
Question: Explain the concept of attention mechanism in NLP.
Answer: The attention mechanism allows neural networks to focus on specific parts of the input sequence when making predictions. It assigns different weights to different parts of the input sequence, enabling the model to pay more attention to relevant information.
Basic data science questions
Question: What is the CRISP-DM methodology in data science?
Answer: CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely used methodology for data mining and analytics projects. It consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
Question: What are the key steps in building a predictive model?
Answer: The key steps in building a predictive model include:
- Data collection and preprocessing
- Exploratory data analysis (EDA)
- Feature engineering and selection
- Model selection and training
- Model evaluation and validation
- Model deployment and monitoring
Question: What is overfitting and how can it be prevented?
Answer: Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns that don’t generalize to new data. It can be prevented by using techniques such as cross-validation, regularization, and early stopping, and by having sufficient data for training.
Question: What is the difference between classification and regression?
Answer: Classification is a supervised learning task where the goal is to categorize input data into discrete classes or categories. Regression, on the other hand, is also a supervised learning task but involves predicting continuous numerical values based on input features.
Question: What is feature selection and why is it important?
Answer: Feature selection is the process of selecting a subset of relevant features from a larger set of features to improve model performance and reduce dimensionality. It is important because using fewer, more relevant features can lead to simpler, more interpretable models that are less prone to overfitting.
Question: Explain the concept of cross-validation.
Answer: Cross-validation is a technique used to assess the performance of a predictive model by splitting the data into multiple subsets (folds), training the model on some folds, and evaluating it on the remaining fold(s). This process is repeated multiple times, and the performance metrics are averaged to obtain a more reliable estimate of the model’s performance.
SQL Interview Questions
Question: What is the difference between SQL and MySQL?
Answer: SQL is a standardized language for querying and managing relational databases, while MySQL is a specific implementation of a relational database management system (RDBMS) that supports SQL. MySQL is one of several RDBMSs that support SQL, alongside others like PostgreSQL, Oracle, and SQL Server.
Question: What are the different types of SQL joins?
Answer: The different types of SQL joins include:
- INNER JOIN: Returns only rows with matching values in both tables.
- LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table and the matched rows from the right table.
- RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table and the matched rows from the left table.
- FULL JOIN (or FULL OUTER JOIN): Returns all rows when there is a match in either table.
Question: What is a primary key in SQL?
Answer: A primary key is a unique identifier for each record in a table. It uniquely identifies each row and ensures that there are no duplicate rows in the table. It is typically defined when creating a table and is enforced by the database management system.
Question: What is the difference between GROUP BY and ORDER BY in SQL?
Answer: GROUP BY is used to group rows that have the same values into summary rows, typically to apply aggregate functions like SUM or COUNT. ORDER BY, on the other hand, is used to sort the result set based on one or more columns, either in ascending or descending order.
Question: What is a subquery in SQL?
Answer: A subquery is a query nested within another query. It can be used to return data that will be used in the main query’s criteria, expressions, or conditions. Subqueries can be used in SELECT, INSERT, UPDATE, or DELETE statements.
Question: What is the difference between DELETE and TRUNCATE in SQL?
Answer: DELETE is a DML (Data Manipulation Language) command used to remove specific rows from a table based on a condition, while TRUNCATE is a DDL (Data Definition Language) command used to remove all rows from a table, effectively resetting the table to its initial state.
Technical Interview Topics
- Technical asked about my projects from my resume in detail
- Leetcode medium question with prompts
- Graphs, and basic time and space complexity questions
- General questions like strength weakness, extracurricular activities, etc.
General Behavioral Interview Questions
Que: What the things you expected from this company
Que: Do you have any bad decisions you made in the past?
Que: How to deal with pressure.
Que: What are the two human qualities you value the most?
Que: What was the time in your life when you were the most stressed and how did you deal with that?
Conclusion
Preparing for a data science and analytics interview at Schlumberger requires a solid understanding of fundamental concepts in data analytics, machine learning, and the oil and gas industry. By familiarizing themselves with these common interview questions and answers, candidates can demonstrate their knowledge and expertise, ultimately increasing their chances of success in securing a position at Schlumberger Ltd.
