Databricks Databricks Certification DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 21:

  While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

  A. 1

  B. 2

  C. 0

  D. n/2

  Correct Answer: A

  Explanation: The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. Basically, the RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is 1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1

• Question 22:

  Which of the following technique can be used to the design of recommender systems?

  A. Naive Bayes classifier

  B. Power iteration

  C. Collaborative filtering

  D. 1 and 3

  E. 2 and 3

  Correct Answer: C

  Explanation: One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation

• Question 23:

  You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?

  A. Root Mean Square Error

  B. Sum of Errors

  C. Mean Absolute Error

  D. Both land 2

  E. Information is not good enough.

  Correct Answer: E

• Question 24:

  You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?

  A. Decrease the number of measures used

  B. Increase the number of clusters

  C. Decrease the number of clusters

  D. Identify additional measures to add to the analysis

  Correct Answer: C

  Explanation: kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations. Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

• Question 25:

  You are working on a Data Science project and during the project you have been gibe a responsibility to interview all the stakeholders in the project. In which phase of the project you are?

  A. Discovery

  B. Data Preparations

  C. Creating Models

  D. Executing Models

  E. Creating visuals from the outcome

  F. Operationnalise the models

  Correct Answer: A

  Explanation: During the discovery phase you will be interviewing all the project stakeholders because they would be having quite a good amount of knowledge for the problem domain you will be working and you also interviewing project sponsors you will get to know what all are the expectations once project get completed. Hence, you will be noting down all the expectations from the project as well as you will be using their expertise in the domain.

• Question 26:

  As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user. So Which recommender system technique benefits most from additional user preference data?

  A. Naive Bayes classifier

  B. Item-based collaborative filtering

  C. Logistic Regression

  D. Content-based filtering

  Correct Answer: B

  Explanation: Item-based scales with the number of items, and user-based scales with the number of users you have. If you have something like a store, you'll have a few thousand items at the most. The biggest stores at the time of writing have around 100,000 items. In the Netflix competition, there were 480,000 users and 17,700 movies. If you have a lot of users: then you'll probably want to go with item-based similarity. For most product-driven recommendation engines, the number of users outnumbers the number of items. There are more people buying items than unique items for sale. Item-based collaborative filtering makes predictions based on users preferences for items. More preference data should be beneficial to this type of algorithm. Content-based filtering recommender systems use information about items or users, and not user preferences, to make recommendations. Logistic Regression, Power iteration and a Naive Bayes classifier are not recommender system techniques.

• Question 27:

  A. It creates the smaller models

  B. It requires the lesser memory to store the coefficients for the model

  C. It reduces the non-significant features e.g. punctuations

  D. Noisy features are removed

  Correct Answer: B

  Explanation: This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing. Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact. The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret. An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

• Question 28:

  Which of the following is a Continuous Probability Distributions?

  A. Binomial probability distribution

  B. Negative binomial distribution

  C. Poisson probability distribution

  D. Normal probability distribution

  Correct Answer: D

• Question 29:

  A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by"

  A. Binomial

  B. Poisson

  C. Normal

  D. Any of the above

  Correct Answer: A

  Explanation: In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials. This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).

• Question 30:

  You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?

  A. You will randomly reduce the number of variables

  B. You will find the correlation among the variables and from their variables are not co- related will be discarded.

  C. You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.

  D. You cannot discard any variable for creating clusters.

  E. You can combine several variables in one variable

  Correct Answer: CE

  Explanation: When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation. You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.