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APTRON Noida, the best Data Science training institute in Noida has published the list of Best Data Science Interview Questions and Answers asked in a variety of interview-sessions conducted at MNCs in real time interview. The Data Science training centre in Noida is working on overall training and development of the students. Training is a responsibility that does not end after completion of Data Science training and certification; in fact, after the successful Data Science certification Course, our 10+ years experienced Data Science trainers conduct training on personality development, email writing, spoken English, resume writing, and mock-interview sessions to boost the confidence and presentation level of the participants. During the Data Science training course, trainers take the students through various lab assignments and develop decision making scenarios using the simulators to provide the first-hand Data Science training experience to the students. Furthermore, we organize recruitment drive and provide 100% placement assistance to the students.
Here are list of Top Answers for Data Science interview questions asked and answers given in sessions mentioned below:
| Data Science Interview Questions | Data Science Interview Answers |
| Why should stop an interactive machine learning algorithm as soon as the performance of the model on a test set stops improving? | To prevent overfitting |
| What is default delimiter for Hive tables? | ^A (Control-A) |
| Certain individuals are more susceptible to autism if they have particular combinations of genes expressed in their DNA. Given a sample of DNA from persons who have autism and a sample of DNA from persons who do not have autism, determine the best technique for predicting whether or not a given individual is susceptible to developing autism? | Linear Regression |
| You are working with a logistic regression model to predict the probability that a user will click on an ad. Your model has hundreds of features, and you’re not sure if all of those features are helping your prediction. Which regularization technique should you use to prune features that aren’t contributing to the model? | Convex |
| Under what two conditions does stochastic gradient descent outperform 2nd-order optimization techniques such as iteratively reweighted least squares? | When the volume of input data is so large and diverse that a 2nd-order optimization technique can be fit to a sample of the data, When the model’s estimates must be updated in real-time in order to account for newobservations. |
| What is the most common reason for a k-means clustering algorithm to returns a sub-optimal clustering of its input? | Non-normal distribution of the input data |
| You have a large m x n data matrix M. You decide you want to perform dimension reduction/clustering on your data and have decide to use the singular value decomposition (SVD; also called principal components analysis PCA) You performed singular value decomposition (SVD; also called principal components analysis or PCA) on you data matrix but you did not center your data first. What does your first singular component describe? | The standard deviation of the data set |
| Many machine learning algorithm involve finding the Global minimum of a convex loss function, primarily because: | The derivative of convex function is always defined |
| Which two techniques should you use to avoid overfitting a classification model to a data set? | Include a small number “noise” features that are not through to be correlated with the dependent variable, Preprocess the data to exclude a typical observation from the model input |
| You are building a k-nearest neighbor classifier (k-NN) on a labeled set of points in a highdimensional space. You determine that the classifier has a large error on the training data. What is the most likely problem? | k-NN compotation does not coverage in high dimensions |
| Which best describes the primary function of Flume? | Flume provides a query languages for Hadoop similar to SQL |
| What are three benefits of running feature selection analysis before filtering a classification model? | Speeds up the model fitting process, Develops an understanding of the importance of different features, Improves the predictive performance of the model |
| When optimizing a function using stochastic gradient descent, how frequently should you update your estimate of the gradient? | Once after every pass through the data set, For each observation with a probability that you choose ahead of time |
| In what format are web server log files usually generated and how must you transform them in order to make them usable for analysis in Hadoop? | XML files that you need to convert to JSON, Text files that require parsing into useful fields |
| Which recommender system technique is domain specific? | User-based collaborative filtering |
| You are about to sample a 100-dimensinal unit-cube. To adequately sample any single given dimension, you need only capture 10 points. How many points do you need to order to sample the complete 100-dimensional unit cube adequately? | 1000 |
| You have acquired a new data source of millions of customer records, and you’ve this data into HDFS. Prior to analysis, you want to change all customer registration to the same date format, make all addresses uppercase, and remove all customer names (for anonymization). Which process will accomplish all three objectives? | Write a script that receives records on stdin, corrects them, and then writes them to stdout. Then, invoke this script in a map-only Hadoop Streaming Job |
| In what way can Hadoop be used to improve the performance of LIoyd’s algorithm for k-means clustering on large data sets? | Distributing the updates of the cluster centroids |
| You have just run a MapReduce job to filter user messages to only those of a selected geographical region. The output for this job in a directory named westUsers, located just below your home directory in HDFS. Which command gathers these records into a single file on your local file system? | Hadoop fs –get westUsers WestUsers.txt |
| You have user profile records in an OLTP database that you want to join with web server logs which you have already ingested into HDFS. What is the best way to acquire the user profile for use in HDFS? | Ingest with Apache Flume, Ingest using Sqoop |
| How can the naiveté of the naive Bayes classifier be advantageous? | It does not require you to make strong assumptions about the data because it is a nonparametric |
| What are two defining features of RMSE (root-mean square error or root-mean-square deviation)? | It is the mean value of recommendations of the K-equal partitions in the input data, It is appropriate for numeric data |
| You want to understand more about how users browse your public website. For example, you war know which pages they visit prior to placing an order. You have a server farm of 200 web server hosting your website. Which is the most efficient process to gather these web servers access logs into your Hadoop cluster for analysis? | Write a MapReduce job with the web servers for mappers and the Hadoop cluster nodes for reducers |
| You want to build a classification model to identify spam comments on a blog. You decide to use the words in the comment text as inputs to your model. Which criteria should you use when deciding which words to use as features in order to contribute to making the correct classification decision? | Choose words for your sample that are most correlated with the Spam label |
| What is the best way to determine the learning rate parameters for stochastic gradient descent when the distribution of the input data shifts over time? | The learning rate should be the value that optimizes the value of the objective function over the first N samples in the dataset |
| Which two machine learning algorithm should you consider as likely to benefit from discretizing continuous features? | Support vector machine, Naïve Bayes |
| What is one limitation encountered by all systems that employ collaborative filtering and use preferences as input. In order to output product recommendations to consumers? | Consumers do not have stable ratings for the same product over time |
| Why is the naive Bayes classifier "naive"? | It assumes Independence between all features |
| Which three metrics are useful in measuring the accuracy and quality of a recommender system? | Tanimoto coefficient, Pearson correlation, Precision |
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