Databricks-Certified-Professional-Data-Scientist Exam Dumps
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Realexamdumps Providing most updated Databricks Certification Question Answers. Here are a few exams:
Databricks Databricks-Certified-Professional-Data-Scientist Frequently Asked Questions
Sample Questions
Realexamdumps Providing most updated Databricks Certification Question Answers. Here are a few sample questions:
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 1
Select the correct option which applies to L2 regularization
Options:
Answer: A, B, C Explanation: Explanation: Explanation :The difference between their properties can be promptly summarized as follows:Table Description automatically generatee
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 2
You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.
Options:
Answer: D Explanation: Explanation: Classification is the process of using several inputs to produce one or more outputs. For example the input might be the income, education and current debt of a customer The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 3
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?
Options:
Answer: C
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 4
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of "pink" items in the test set of color balls?
Options:
Answer: C Explanation: Explanation: Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency in the training set. The method of maximum likelihood corresponds to many well-known estimation methods in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to measure the height of every single penguin in a population due to cost or time constraints. Assuming that the heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE would accomplish this by taking the mean and variance as parameters and finding particular parametric values that make the observed results the most probable (given the model).In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the "agreement" of the selected model with the observed data, and for discrete random variables it indeed maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems. However in some complicated problems, difficulties do occur: in such problems, maximum-likelihood estimators are unsuitable or do not exist.
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 5
A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?
Options:
Answer: C Explanation: Explanation: In this problem you have been given high-dimensional independent variables like yes, no: 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 forestt
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 6
Which of the following skills a data scientists required?
Options:
Answer: B, C, D Explanation: Explanation: Yes a data scientists should have combination of skills like to solve the complex problem he should be creative as well as able to find new solutions and use of existing data. And solve the problem skills required are programming as currently we see SAS, R: Python, Spark, Java and SPSS even day by day new technologies are coming.To apply various existing and new algorithm using Machine Learning, or Al it require good mathematics and statistics skills (Where the programmer feels, weaknesses). Another skill required is using visualization techniques like Qlik, Tableau etd
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 7
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
Options:
Answer: B Explanation: Explanation: This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). soP(E1|E2) = P(E1.E2)/P(E2)P(E1,E2,E3)/P(E2,E3)If the events are A and B respectively, this is said to be "the probability of A given B"It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 8
You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.
Options:
Answer: C Explanation: Explanation: Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.
Databricks Databricks-Certified-Professional-Data-Scientist Sample Question 9
Clustering is a type of unsupervised learning with the following goals
Options:
Answer: E Explanation: Explanation: 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.