DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Online Practice Questions and Answers

Correct Answer: ABC

Explanation: In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high- dimensional feature spaces.

Correct Answer: A

Explanation: The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. Basically, the RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is 1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1

Correct Answer: A

Explanation: Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.

Correct Answer: D

Explanation: The total number of clicks is the sum of the number of men and women. The sum of two Poisson random variables also follows a Poisson distribution with rate equal to the sum of their rates. The Normal and Binomial distribution can approximate the Poisson distribution in certain cases, but the expressions above do not approximate Poisson(X+Y).

Correct Answer: A

Explanation: The entire problem can be solved using below method Binomial: 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? Poisson: 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 Poisson: 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. Normal: 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

Correct Answer: B

Explanation: Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

Correct Answer: D

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.

Correct Answer: ABC

Explanation: : Supervised learning asks the machine to learn from our data when we specify a target variable.

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

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?

Correct Answer: A

Explanation: Sup Supervised learning tasks Classification Regression k-Nearest Neighbors Linear Naive Bayes Locally weighted linear Support vector machines Ridge Decision trees Lasso Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window

Correct Answer: C

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.