Provide Databricks Databricks-Certified-Professional-Data-Scientist Dumps Updated May 06, 2023 With 140 QA's [Q20-Q44]

Q20. Question-34. Stories appear in the front page of Digg as they are “voted up” (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members. Which of the following technique is used to make such recommendation engine?

Naive Bayes classifier

Collaborative filtering

Logistic Regression

Content-based filtering

Q21. A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.
Above is an example of

Linear Regression

Logistic Regression

Recommendation system

Maximum likelihood estimation

Hierarchical linear models

Q22. Select the correct statement which applies to K-Nearest Neighbors

No Assumption about the data

Computationally expensive

Require less memory

Works with Numeric Values

Q23.

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

Q24. Select the sequence of the developing machine learning applications
A) Analyze the input data
B) Prepare the input data
C) Collect data
D) Train the algorithm
E) Test the algorithm
F) Use It

A, B, C, D, E, F

C, B, A, D, E, F

C, A, B, D, E, F

C, B, A, D, E, F

Explanation
1 Collect data. You could collect the samples by scraping a website and extracting data: or you could get information from an RSS feed or an API. You could have a device collect wind speed measurements and send them to you, or blood glucose levels, or anything you can measure. The number of options is endless. To save some time and effort you could use publicly available data
2 Prepare the input data. Once you have this data, you need to make sure it’s in a useable format. The format we’ll be using in this book is the Python list. We’ll talk about Python more in a little bit, and lists are reviewed in appendix A.
The benefit of having this standard format is that you can mix and match algorithms and data sources. You may need to do some algorithm-specific formatting here. Some algorithms need features in a special format, some algorithms can deal with target variables and features as strings, and some need them to be integers. We’ll get to this later but the algorithm-specific formatting is usually trivial compared to collecting data.
3 Analyze the input data. This is looking at the data from the previous task. This could be as simple as looking at the data you’ve parsed in a text editor to make sure steps 1 and 2 are actually working and you don’t have a bunch of empty values. You can also look at the data to see if you can recognize any patterns or if there’s anything obvious^ such as a few data points that are vastly different from the rest of the set. Plotting data in one: two, or three dimensions can also help. But most of the time you’ll have more than three features, and you can’t easily plot the data across all features at one time. You could, however use some advanced methods we’ll talk about later to distill multiple dimensions down to two or three so you can visualize the data.
4 If you’re working with a production system and you know what the data should look like, or you trust its source: you can skip this step. This step takes human involvement, and for an automated system you don’t want human involvement. The value of this step is that it makes you understand you don’t have garbage coming in.
5 Train the algorithm. This is where the machine learning takes place. This step and the next step are where the “core” algorithms lie, depending on the algorithm.You feed the algorithm good clean data from the first two steps andextract knowledge or information. This knowledge you often store in a formatthat’s readily useable by a machine for the next two steps.In the case of unsupervised learning, there’s no training step because youdon’t have a target value. Everything is used in the next step.
6 Test the algorithm. This is where the information learned in the previous step isput to use. When you’re evaluating an algorithm, you’ll test it to see how well itdoes. In the case of supervised learning, you have some known values you can use to evaluate the algorithm. In unsupervised learning, you may have to use some other metrics to evaluate the success. In either case, if you’re not satisfied, you can go back to step 4, change some things, and try testing again. Often thecollection or preparation of the data may have been the problem, and you’ll have to go back to step 1.
7 Use it. Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1-5.

Q25. 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?

Logistic regression with L1 regularization, to prevent overfitting

Naive Bayes, because Bayesian methods act as regularlizers

k-nearest neighbors, because it uses local neighborhoods to classify examples

Random forest because it is an ensemble method

Explanation
Logistic regression is widely used in machine learning for classification problems. It is well-known that regularization is required to avoid over-fitting, especially when there is a only small number of training examples, or when there are a large number of parameters to be learned. In particular L1 regularized logistic regression is often used for feature selection, and has been shown to have good generalization performance in the presence of many irrelevant features. (Ng 2004; Goodman 2004) Unregularized logistic regression is an unconstrained convex optimization problem with a continuously differentiate objective function. As a consequence, it can be solved fairly efficiently with standard convex optimization methods, such as Newton’s method or conjugate gradient. However, adding the L1 regularization makes the optimization problem com-putationally more expensive to solve. If the L1 regulariza-tion is enforced by an L1 norm constraint on the parameLogistic regression is a classifier and L1 regularization tends to produce models that ignore dimensions of the input that are not predictive. This is particularly useful when the input contains many dimensions, k-nearest neighbors classification is also a classification technique, but relies on notions of distance. In a high-dimensional space, most every data point is “far” from others (the curse of dimensionality) and so these techniques break down. Naive Bayes is not inherently regularizing. Random forests represent an ensemble method; but an ensemble method is not necessarily more suitable to high-dimensional data.
Practically, I think the biggest reasons for regularization are 1) to avoid overfitting by not generating high coefficients for predictors that are sparse. 2) to stabilize the estimates especially when there’s collinearity in the data.
1) is inherent in the regularization framework. Since there are two forces pulling each other in the objective function, if there’s no meaningful loss reduction, the increased penalty from the regularization term wouldn’t improve the overall objective function. This is a great property since a lot of noise would be automatically filtered out from the model. To give you an example for 2), if you have two predictors that have same values, if you just run a regression algorithm on it since the data matrix is singular your beta coefficients will be Inf if you try to do a straight matrix inversion. But if you add a very small regularization lambda to it, you will get stable beta coefficients with the coefficient values evenly divided between the equivalent two variables. For the difference between L1 and L2, the following graph demonstrates why people bother to have L1 since L2 has such an elegant analytical solution and is so computationally straightforward. Regularized regression can also be represented as a constrained regression problem (since they are Lagrangian equivalent). The implication of this is that the L1 regularization gives you sparse estimates. Namely, in a high dimensional space, you got mostly zeros and a small number of non-zero coefficients. This is huge since it incorporates variable selection to the modeling problem. In addition, if you have to score a large sample with your model, you can have a lot of computational savings since you don’t have to compute features(predictors) whose coefficient is 0. I personally think L1 regularization is one of the most beautiful things in machine learning and convex optimization. It is indeed widely used in bioinformatics and large scale machine learning for companies like Facebook, Yahoo, Google and Microsoft.

Q26. Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

Cluster Density

Support Vector Count

Mean Absolute Error

Sum of Absolute Errors

Q27. Google Adwords studies the number of men, and women, clicking the advertisement on search engine during the midnight for an hour each day.
Google find that the number of men that click can be modeled as a random variable with distribution Poisson(X), and likewise the number of women that click as Poisson(Y).
What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour
?

Binomial(X+Y,X+Y)

Poisson(X/Y)

Normal(X+Y(M+Y)1/2)

Poisson(X+Y)

Q28. 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)

Q29. 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

Q30. While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

1

2

0

n/2

Q31. In which of the scenario you can use the linear regression model?

Predicting Home Price based on the location and house area

Predicting demand of the goods and services based on the weather

Predicting tumor size reduction based on input as number of radiation treatment

Predicting sales of the text book based on the number of students in state

Q32. 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)

Q33. 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

Q34. 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

Q35. What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

1/3

2/3

1/6

2/6

Q36. In which phase of the analytic lifecycle would you expect to spend most of the project time?

Discovery

Data preparation

Communicate Results

Operationalize

Q37. 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?

Logistic Regression

Decision Trees

Linear Regression

ARIMA

Q38. Which of the following question statement falls under data science category?

What happened in last six months?

How many products have been sold in a last month?

Where is a problem for sales?

Which is the optimal scenario for selling this product?

What happens, if these scenario continues?

Q39. 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

Q40. 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.

Q41. You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?

Identify additional measures to add to the analysis

Remove one of the measures

Decrease the number of clusters

Increase the number of clusters

Q42. Which analytical method is considered unsupervised?

may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

Naive Bayesian classifier

Decision tree

Linear regression

K-means clustering

Q43. Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

Q44. 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

Provide Databricks Databricks-Certified-Professional-Data-Scientist Dumps Updated May 06, 2023 With 140 QA’s [Q20-Q44]

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