Free Databricks Certified Professional Data Scientist Exam Databricks-Certified-Professional-Data-Scientist Exam Practice Test

Which of the following could be features?

• Words in the document
  
• Symptoms of a diseases
  
• Characteristics of an unidentified object
  
• 0nly 1 and 2
  
• All 1,2 and 3 are possible
  Any dataset that can be turned into lists of features. A feature is simply something that is either present or absent for a given item. In the case of documents, the features are the words in the document but they could also be characteristics of an unidentified object symptoms of a disease, or anything else that can be said to be present of absent.
  

You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

• Naive Bayes
  
• Laplace Smoothing
  
• Logistic Regression
  
• All of the above
  Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.
  

Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like 'YES' or 'NO', then which of the following technique best fit.

• Support vector machines
  
• Naive Bayes
  
• Logistic regression
  
• Random decision forests
  
• All of the above
  In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
  * Support vector machines
  * Naive Bayes
  * Logistic regression
  * Random decision forests
  

In which of the scenario you can use the regression to predict the values

• Samsung can use it for mobile sales forecast
  
• Mobile companies can use it to forecast manufacturing defects
  
• Probability of the celebrity divorce
  
• Only 1 and 2
  
• All 1 ,2 and 3
  Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.
  

Reducing the data from many features to a small number so that we can properly visualize it intwo or three dimensions. It is done in_______

• supervised learning
  
• un-supervised learning
  
• k-Nearest Neighbors
  
• Support vector machines
  The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions
  

In unsupervised learning which statements correctly applies

• It does not have a target variable
  
• Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
  
• telling the machine Predict Y for our data X
  In unsupervised learning we don't have a target variable as we did in
  classification and regression.
  Instead of telling the machine Predict Y for our data X, we're asking What can you
  tell me about X?
  Things we ask the machine to tell us about
  X may be What are the six best groups we can make out of X? or What three
  features occur together most frequently in X?
  

In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

• Discovery
  
• Data Preparation
  
• Model Building
  
• Communicate Results
  

Clustering is a type of unsupervised learning with the following goals

• Maximize a utility function
  
• Find similarities in the training data
  
• Not to maximize a utility function
  
• 1 and 2
  
• 2 and 3
  type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data.
  The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.
  

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

• Binomial
  
• Poisson
  
• Normal
  
• Any of the above
  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).
  

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?

• Discovery
  
• Data Preparations
  
• Creating Models
  
• Executing Models
  
• Creating visuals from the outcome
  
• Operationnalise the models
  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.