Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e". The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?
A. Option A
B. Option B
C. Option C
D. Option D
What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?
A. 1/3
B. 2/3
C. 1/6
D. 2/6
Refer to the Exhibit.
In the Exhibit, the table shows the values for the input Boolean attributes "A", "B", and "C". It also shows the values for the output attribute "class". Which decision tree is valid for the data?
A. Tree A
B. Tree B
C. Tree C
D. Tree D
In which of the scenario you can use the regression to predict the values?
A. Samsung can use it for mobile sales forecast
B. Mobile companies can use it to forecast manufacturing defects
C. Probability of the celebrity divorce
D. Only 1 and 2
E. All 1 ,2 and 3
You are analyzing data in order to build a classifier model. You discover non-linear data and discontinuities that will affect the model. Which analytical method would you recommend?
A. Logistic Regression
B. Decision Trees
C. Linear Regression
D. ARIMA
Select the correct statement which applies to Supervised learning
A. We asks the machine to learn from our data when we specify a target variable.
B. Lesser machine's task to only divining some pattern from the input data to get the target variable
C. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B. Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B. 40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?
A. Bays Theorem
B. Poisson Distribution
C. Binomial Distribution
D. None of the above
You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?
A. Logistic regression with L1 regularization, to prevent overfitting
B. Naive Bayes, because Bayesian methods act as regularlizers
C. k-nearest neighbors, because it uses local neighborhoods to classify examples
D. Random forest because it is an ensemble method
If you are trying to predict or forecast a discrete target value, then which is the correct options?
A. Supervised Learning regression algorithms
B. Supervised Learning classification algorithms
C. Un supervised Learning
D. Density estimation algorithm
Select the correct algorithm of unsupervised algorithm
A. K-Nearest Neighbors
B. K-Means
C. Support Vector Machines
D. Naive Bayes
Nowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Databricks exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST exam preparations and Databricks certification application, do not hesitate to visit our Vcedump.com to find your solutions here.