Online Access Free Databricks-Certified-Professional-Data-Scientist Practice Test

You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?

• A.Include least mutual information with other selected features as a feature selection criterion
• B.Evaluate a model that only includes the top 100 words
• C.Decrease the size of our training data
• D.Include the number of times each of the words appears in the book in your model

A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?

• A.Association Rules
• B.K Means Clustering
• C.Naive Bayesian
• D.Logistic Regression

What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

• A.The lowest cost clustering subject to a stability constraint
• B.The most stable clustering
• C.The most stable clustering subject to a minimal cost constraint
• D.The lowest cost clustering

Which of the following question statement falls under data science category?

• A.What happens, if these scenario continues?
• B.Which is the optimal scenario for selling this product?
• C.How many products have been sold in a last month?
• D.Where is a problem for sales?
• E.What happened in last six months?

Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation, car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.
Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of
1 3 to each of the three possibilities. Which of the following method the boss will use to estimate of the probability that Bob drove to work?

• A.None of the above
• B.Linear regression
• C.Naive Bayes
• D.Random decision forests