Databricks Databricks Certifications DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 111:

  Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?

  A. Define the process to maintain the model

  B. Try different analytical techniques

  C. Try different variables

  D. Transform existing variables

  Correct Answer: A

  Explanation: Operationalize In the final phase, the team communicates the benefits of the project more broadly and sets up a pilot project to deploy the work in a controlled way before broadening the work to a full enterprise or ecosystem of users. In Phase 4. the team scored the model in the analytics sandbox.

• Question 112:

  Which of the following are point estimation methods?

  A. MAP

  B. MLE

  C. MMSE

  Correct Answer: ABC

  Explanation: Point estimators

  minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.

  best linear unbiased estimator (BLUE)

  minimum mean squared error (MMSE)

  median-unbiased estimator, minimizes the risk of the absolute-error loss function

  maximum likelihood (ML)

  method of moments, generalized method of moments

• Question 113:

  Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything about this conversation, the probability that he was speaking to a woman is 50% (assuming the train had an equal number of men and women and the speaker was as likely to strike up a conversation with a man as with a woman). Now suppose he also told you that his conversational partner had long hair. It is now more likely he was speaking to a woman, since women are more likely to have long hair than men.____________ can be used to calculate the probability that the person was a woman.

  A. SVM

  B. MLE

  C. Bayes' theorem

  D. Logistic Regression

  Correct Answer: C

  Explanation: To see how this is done, let W represent the event that the conversation was held with a woman, and L denote the event that the conversation was held with a long-haired person. It can be assumed that women constitute half the

  population for this example. So, not knowing anything else, the probability that W occurs is P(W) = 0.5. Suppose it is also known that 75% of women have long hair which we denote as P(L |W) = 0.75 (read: the probability of event L given

  event W is 0.75, meaning that the probability of a person having long hair (event "L"): given that we already know that the person is a woman ("event W") is 75%). Likewise, suppose it is known that 15% of men have long hair, or P(L |M) =

  0.15; where M is the complementary event of W: i.e.; the event that the conversation was held with a man (assuming that every human is either a man or a woman).

  Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, P(W |L). Using the formula for Bayes' theorem, we have:

  

  Text Description automatically generated with low confidence where we have used the law of total probability to expand P(L),

  The numeric answer can be obtained by substituting the above values into this formula (the algebraic multiplication is annotated using " *", the centered dot). This yields

  

  A picture containing table i.e., the probability that the conversation was held with a woman, given that the person had long hair is about 83%. More examples are provided below.

• Question 114:

  In unsupervised learning which statements correctly applies?

  A. It does not have a target variable

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

  C. telling the machine Predict Y for our data X

  Correct Answer: AB

  Explanation: 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?

• Question 115:

  Refer to the exhibit.

  

  You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain. Based on this information, on which attribute would you expect the next split to be in the decision tree?

  A. Credit Score

  B. Age

  C. Income

  D. Gender

  Correct Answer: A

• Question 116:

  RMSE measures error of a predicted:

  A. Numerical Value

  B. Categorical values

  C. For booth Numerical and categorical values

  Correct Answer: A

• Question 117:

  Which of the following statement is true for the R square value in the regression model?

  A. When R square =1 , all the residuals are equal to 0

  B. When R square =0, all the residual are equal to 1

  C. R square can be increased by adding more variables to the model.

  D. R-squared never decreases upon adding more independent variables.

  Correct Answer: ACD

  Explanation: R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.

• Question 118:

  A problem statement is given as below

  Hospital records show that of patients suffering from a certain disease, 75% die of it. What is the probability that of 6 randomly selected patients, 4 will recover?

  Which of the following model will you use to solve it.

  A. Binomial

  B. Poisson

  C. Normal

  D. Any of the above

  Correct Answer: A

• Question 119:

  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...

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

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

  C. L1 gives Non-sparse output while L2 gives sparse outputs

  D. None of the above

  Correct Answer: A

  Explanation: 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 just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows:

  L1 regularization on least squares:

  

• Question 120:

  What is the considerable difference between L1 and L2 regularization?

  A. L1 regularization has more accuracy of the resulting model

  B. Size of the model can be much smaller in L1 regularization than that produced by L2- regularization

  C. L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.

  D. All of the above are correct

  Correct Answer: B

  Explanation: The two most common regularization methods are called L1 and L2 regularization. L1 regularization penalizes the weight vector for its L1-norm (i.e. the sum of the absolute values of the weights), whereas L2 regularization uses its L2-norm. There is usually not a considerable difference between the two methods in terms of the accuracy of the resulting model (Gao et al 2007), but L1 regularization has a significant advantage in practice. Because many of the weights of the features become zero as a result of L1- regularized training, the size of the model can be much smaller than that produced by L2- regularization. Compact models require less space on memory and storage, and enable the application to start up quickly. These merits can be of vital importance when the application is deployed in resource-tight environments such as cell-phones. Regularization works by adding the penalty associated with the coefficient values to the error of the hypothesis. This way, an accurate hypothesis with unlikely coefficients would be penalized whila a somewhat less accurate but more conservative hypothesis with low coefficients would not be penalized as much.