Databricks Certified Professional Data Scientist Exam Dumps

dumps2021-11-04T03:39:35+08:00

Databricks Certified Professional Data Scientist exam dumps are available, which are good enough for you to pass the test. There are 60 questions in real Databricks Certified Professional Data Scientist exam, and you have 120 minutes to complete all the questions. The minimum passing score of Databricks Certified Professional Data Scientist exam is 70%. Free Databrick Certified Professional Data Scientist exam dumps are available below.

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1. Select the correct statement which applies to K-Nearest Neighbors

No Assumption about the data

Computationally expensive

Require less memory

Works with Numeric Values

2. Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...

L2 is the sum of the square of the weights, while L1 is just the sum of the weights

L1 is the sum of the square of the weights, while L2 is just the sum of the weights

L1 gives Non-sparse output while L2 gives sparse outputs

None of the above

3. 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?

2.4

24 0

.24

.48

4.8

4. Select the correct option which applies to L2 regularization

Computational efficient due to having analytical solutions

Non-sparse outputs

No feature selection

5. What are the key outcomes of the successful analytical projects?

Code of the model

Technical specifications

Presentations for the Analysts

Presentation for Project Sponsors

6. Refer to exhibit

You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1. Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C. The results of the regression are seen in the exhibit. You cannot request additional data.

What is a way that you could try to increase the R2 of the model without artificially inflating it?

Create clusters based on the data and use them as model inputs

Force all 15 variables into the model as independent variables

Create interaction variables based only on variables A, B, and C

Break variables A, B, and C into their own univariate models

7. Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct

It means that your predictions are on average one star off of what people really think

It means that your predictions are on average two star off of what people really think

It means that your predictions are on average three star off of what people really think

It means that your predictions are on average four star off of what people really think

8. 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

9. You recommend a movie with three stars but the user loves it (he'd rate it five stars).

So which statement correctly applies?

In both cases, the contribution to the RMSE is the same

In both cases, the contribution to the RMSE is the different

In both cases, the contribution to the RMSE, could varies

None of the above

10. 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

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11. What describes a true property of Logistic Regression method?

It handles missing values well.

It works well with discrete variables that have many distinct values.

It is robust with redundant variables and correlated variables.

It works well with variables that affect the outcome in a discontinuous way.

12. Which of the following is not a correct application for the Classification?

credit scoring

tumor detection

image recognition

drug discovery

13. Which of the following steps you will be using in the discovery phase?

What all are the data sources for the project?

Analyze the Raw data and its format and structure.

What all tools are required, in the project?

What is the network capacity required

What Unix server capacity required?

14. Select the correct statement which applies to logistic regression

Computationally inexpensive, easy to implement knowledge representation easy to interpret

May have low accuracy

Works with Numeric values

15. Which of the following is a correct example of the target variable in regression (supervised learning)?

Nominal values like true, false

Reptile, fish, mammal, amphibian, plant, fungi

Infinite number of numeric values, such as 0.100, 42.001, 1000.743..

All of the above

16. Suppose there are three events then which formula must always be equal to P (E1|E2, E3)?

P (E1, E2, E3) P(E1)/P (E2:E3)

P (E1, E2; E3)/P (E2, E3)

P (E1, E2|E3) P(E2|E3) P(E3)

P (E1, E2|E3) P(E3)

P (E1, E2, E3) P(E2) P(E3)

17. 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

18. Select the correct statement which applies to Principal component analysis (PCA)

Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.

Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables

Increase the dimensionality of the data set.

1 and 3 are correct

1 and 2 are correct

19. Projecting a multi-dimensional dataset onto which vector has the greatest variance?

first principal component

first eigenvector

not enough information given to answer

second eigenvector

second principal component

20. Which technique you would be using to solve the below problem statement? "What is the probability that individual customer will not repay the loan amount?"

Classification

Clustering

Linear Regression

Logistic Regression

Hypothesis testing

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21. Select the correct statement which applies to logistic regression

Computationally inexpensive, easy to implement knowledge representation easy to interpret

May have low accuracy

Works with Numeric values

Only 1 and 3 are correct

All 1, 2 and 3 are correct

22. Reducing the data from many features to a small number so that we can properly visualize it in

two or three dimensions. It is done in_______

supervised learning

un-supervised learning

k-Nearest Neighbors

Support vector machines

23. 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

24. In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because

The normalizing constant is always very close to 1

The normalizing constant only has a small impact on the maximum likelihood

The normalizing constant is often zero and can cause division by zero

The normalizing constant doesn't impact the maximizing value

25. Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

26. Your customer provided you with 2. 000 unlabeled records three groups.

What is the correct analytical method to use?

Semi Linear Regression

Logistic regression

Naive Bayesian classification

Linear regression

K-means clustering

27. You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual.

Which algorithm is the most appropriate for this study?

Association rules

Decision trees

Linear regression

K-means clustering

28. If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

P(E1)/P(E2)

P(E1+E2)/P(E1)

P(E2)/P(E1)

P(E2)/(P(E1+E2)

29. Select the correct statement which applies to Supervised learning

We asks the machine to learn from our data when we specify a target variable.

Lesser machine's task to only divining some pattern from the input data to get the target variable

Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?

30. Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:

In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.

Select the correct statement

Precision is low, which means the classifier is predicting positives best

Precision is low, which means the classifier is predicting positives poorly

problem domain has a major impact on the measures that should be used to evaluate a classifier within it

1 and 3

2 and 3

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31. What are the advantages of the mutual information over the Pearson correlation for text classification problems?

The mutual information has a meaningful test for statistical significance.

The mutual information can signal non-linear relationships between the dependent and independent variables.

The mutual information is easier to parallelize.

The mutual information doesn't assume that the variables are normally distributed.

32. Refer to image below

Option A

Option B

Option C

Option D

33. 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)?

The lowest cost clustering subject to a stability constraint

The lowest cost clustering

The most stable clustering subject to a minimal cost constraint

The most stable clustering

34. Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because

The penalty term is not differentiate

The second derivative is not constant

The objective function is not convex

The constraints are quadratic

35. A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P (A|B).

1/3

2/3

1/6

2/6

36. The figure below shows a plot of the data of a data matrix M that is 1000 x 2.

Which line represents the first principal component?

yellow

blue

Neither

37. In which of the following scenario we can use naTve Bayes theorem for classification

Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

To classify whether an email is spam or not spam

To identify whether a fruit is an orange or not based on features like diameter, color and shape

38. Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367.

However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.

Naive Bayes

Laplace Smoothing

Logistic Regression

Linear Regression

39. Which of the following problem you can solve using binomial distribution

A manufacturer of metal pistons finds that on the average: 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?

A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies

Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.

It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20.03 mm and 20.08 mm

40. Support vector machines (SVMs) are a set of supervised learning methods used for

Linear classification

Non-linear classification

Regression

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41. What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

Expected value

Variance

Linear regression

Quantiles

42. You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related.

What is the best solution for that?

You will take cube root of height

You will take square root of weight

You will take square of the height.

You would consider using BMI (Body Mass Index)

Databricks Certified Professional Data Scientist Exam Dumps

Databricks Certified Professional Data Scientist Exam Dumps

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