In the fast-paced world of data science and analytics, landing a job at a reputable company like TELUS International requires more than just technical skills. Being prepared for the interview process with a solid understanding of key concepts and thoughtful responses can make all the difference. To help you ace your interview, let’s delve into some of the top questions you might encounter and the expert answers to guide you through.
Table of Contents
Technical Interview Questions
Question: How does querying Hive is different from Ms-SQL or Oracle?
Answer: Querying Hive is different from MS-SQL or Oracle mainly due to its nature as a distributed data warehouse system built on top of Hadoop. Hive uses a SQL-like language called HiveQL, which allows users to write queries similar to SQL. However, HiveQL queries are executed as MapReduce jobs, making it suitable for processing large-scale, batch-oriented data. On the other hand, MS-SQL and Oracle are traditional relational database management systems (RDBMS) with their own SQL dialects, optimized for transactional processing and handling smaller datasets.
Question: Difference between bucketing and partitioning.
Answer: Bucketing and partitioning are both techniques used in Hive to improve query performance.
- Partitioning divides the data into multiple directories based on the defined partition columns. This helps in efficiently pruning the data while querying, as the query engine can skip entire partitions that are not relevant to the query predicates.
- Bucketing, on the other hand, divides data into fixed-sized buckets based on a hash of the columns’ values. It is useful for evenly distributing data across the buckets, aiding in more balanced storage and faster querying by reducing the amount of data scanned.
Question: Tell me about Hive window functions.
Answer: Hive window functions allow you to perform calculations across a set of rows related to the current row within a partition of a result set. They operate similarly to aggregate functions but do not collapse the result set into a single row. Instead, they return a value for each row based on a window of rows defined by the ORDER BY and PARTITION BY clauses.
Some common Hive window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), and LEAD(). These functions enable tasks such as calculating running totals, computing moving averages, finding the rank of rows within a partition, and accessing data from preceding or following rows.
Question: Explain Rank vs dense tank.
Answer:
- RANK(): This function assigns a unique rank to each distinct row. If there are ties in the data, the ranks are skipped, resulting in non-consecutive ranks. For example, if two rows tie for the second rank, both will get a rank of 2, and the next row will have a rank of 4.
- DENSE_RANK(): Unlike RANK(), DENSE_RANK() also assigns a unique rank to each distinct row, but it does not skip ranks in case of ties. This means that if two rows tie for the second rank, both will get a rank of 2, and the next row will have a rank of 3.
Question: What is parameter tuning?
Answer: Parameter tuning, also known as hyperparameter tuning, refers to the process of selecting the optimal values for the parameters that define the behavior and performance of a machine learning model. These parameters are not learned during the training process, unlike model parameters, but are set prior to training and affect how the algorithm learns from the data.
Question: How do you tune a logistic regression model?
Answer: Here’s a concise guide on tuning a logistic regression model:
- Select Regularization Parameter (C): Choose a range of C values (controls regularization strength) and use cross-validation to find the optimal value that minimizes loss.
- Choose Solver Algorithm: Test different solvers (‘liblinear’, ‘lbfgs’, ‘sag’, ‘newton-cg’) based on dataset size and regularization type (L1 or L2) to find the best-performing one.
- Decide Penalty Type: Test L1 (Lasso) and L2 (Ridge) penalties to see which yields better results; L1 can produce sparser models, while L2 penalizes large coefficients more evenly.
- Perform Feature Engineering: Enhance features by creating new ones, scaling, or transforming existing ones to improve model performance.
- Utilize Cross-Validation: Evaluate model performance using cross-validation to ensure generalization and prevent overfitting during parameter tuning.
Question: Explain lift analysis.
Answer: Lift analysis, also known as gain analysis, measures the effectiveness of a predictive model by comparing its predictions to a random guess or baseline model. It involves:
- Sorting data based on predicted probabilities.
- Segmenting the data into bins.
- Calculating the ratio of observed event rates to average rates in each bin.
- The resulting lift curve shows how much better the model predicts the target event compared to random guessing, aiding in targeting strategies in marketing or business analytics.
Question: What is AUC?
Answer: AUC stands for Area Under the ROC Curve. It is a metric used to evaluate the performance of a binary classification model. The ROC (Receiver Operating Characteristic) curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for different threshold values of the model. AUC represents the area under this curve, where a higher AUC value (closer to 1) indicates better model performance in distinguishing between the two classes.
Question: How do you test a model for over or under fitting?
Answer: Testing a model for overfitting or underfitting involves evaluating its performance on both training and testing datasets. Here’s how to do it:
Overfitting:
- Signs: Model performs very well on the training data but poorly on unseen data.
- Testing: Compare model performance on training and testing datasets.
- Indicators: A large gap between training and testing accuracy or loss indicates overfitting.
- Solution: Regularization techniques like L1/L2, reducing model complexity, or increasing training data can help.
Underfitting:
- Signs: Model performs poorly on both training and testing datasets.
- Testing: Evaluate model performance on training and testing datasets.
- Indicators: Low training and testing accuracy or high bias suggest underfitting.
- Solution: Increase model complexity, add more features, or use more sophisticated algorithms.
Statistics and ML Interview Questions
Question: What is the Central Limit Theorem?
Answer: The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.
Question: Explain the difference between Type I and Type II errors.
Answer: Type I error (False Positive) occurs when a true null hypothesis is rejected, while Type II error (False Negative) occurs when a false null hypothesis is not rejected.
Question: What is p-value? How is it used in hypothesis testing?
Answer: The p-value is the probability of obtaining results as extreme as the observed results under the assumption that the null hypothesis is true. In hypothesis testing, a smaller p-value suggests stronger evidence against the null hypothesis.
Question: What is the difference between Supervised and Unsupervised Learning?
Answer: Supervised learning uses labeled data with input-output pairs to train the model to make predictions, while unsupervised learning uses unlabeled data to find hidden patterns or groupings.
Question: Explain the Bias-Variance Tradeoff in machine learning.
Answer: The Bias-Variance Tradeoff refers to the balance between the model’s ability to capture the underlying patterns in the data (low bias) and its sensitivity to variations in the training data (low variance). A model with high bias tends to underfit, while a model with high variance tends to overfit.
Question: What is Cross-Validation? Why is it important?
Answer: Cross-Validation is a technique used to evaluate the performance of a model by splitting the dataset into multiple subsets, training the model on some subsets, and testing it on others. It helps in assessing how well the model generalizes to unseen data and avoids overfitting.
Question: Explain the difference between Regression and Classification.
Answer: Regression is used to predict continuous output values, such as house prices or stock prices, while Classification is used to predict discrete output classes, such as whether an email is spam or not.
Conclusion
Preparing for a data science and analytics interview at TELUS International requires a blend of technical knowledge, problem-solving skills, and an understanding of the company’s goals. By mastering these key questions and tailoring your responses to showcase both your expertise and alignment with TELUS International’s vision, you’ll be well-equipped to impress the interviewers and secure your dream role in this dynamic field.
