Databricks-Certified-Professional-Data-Scientist PDF Pass Leader, Databricks-Certified-Professional-Data-Scientist Latest Real Test
Valid Databricks-Certified-Professional-Data-Scientist Test Answers & Databricks-Certified-Professional-Data-Scientist Exam PDF
Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
NEW QUESTION 40
Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B.Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B.40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?
- A. None of the above
- B. Bays Theorem
- C. Binomial Distribution
- D. Poisson Distribution
Answer: B
NEW QUESTION 41
In which phase of the analytic lifecycle would you expect to spend most of the project time?
- A. Discovery
- B. Data preparation
- C. Communicate Results
- D. Operationalize
Answer: B
Explanation:
Explanation
In the data preparation phase of the Data Analytics Lifecycle, the data range and distribution can be obtained.
If the data is skewed, viewing the logarithm of the data (if it's all positive) can help detect structures that might otherwise be overlooked in a graph with a regular, nonlogarithmic scale.
When preparing the data, one should look for signs of dirty data, as explained in the previous section. Examining if the data is unimodal or multimodal will give an idea of how many distinct populations with different behavior patterns might be mixed into the overall population. Many modeling techniques assume that the data follows a normal distribution. Therefore, it is important to know if the available dataset can match that assumption before applying any of those modeling techniques.
NEW QUESTION 42
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).
- A. 2/3
- B. 1/3
- C. 2/6
- D. 1/6
Answer: D
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION 43
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?
- A. The data is unformatted.
- B. There are missing values in the data.
- C. There are categorical variables in the model.
- D. There is not enough data to create a test set.
Answer: D
NEW QUESTION 44
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?
- A. Naive Bayes classifier
- B. Logistic Regression
- C. Collaborative filtering
- D. Content-based filtering
Answer: C
Explanation:
Explanation
One scenario of collaborative filtering application is to recommend interesting or popular information as judged by the community. As a typical example, 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.
NEW QUESTION 45
What type of output generated in case of linear regression?
- A. Discrete Variable
- B. Any of the Continuous and Discrete variable
- C. Values between 0 and 1
- D. Continuous variable
Answer: D
Explanation:
Explanation
Linear regression model generate continuous output variable.
NEW QUESTION 46
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
- A. It means that your predictions are on average four star off of what people really think
- B. It means that your predictions are on average two star off of what people really think
- C. It means that your predictions are on average three star off of what people really think
- D. It means that your predictions are on average one star off of what people really think
Answer: D
NEW QUESTION 47
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
- A. The penalty term is not differentiate
- B. The second derivative is not constant
- C. The objective function is not convex
- D. The constraints are quadratic
Answer: A
Explanation:
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.
Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum.
You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.
NEW QUESTION 48
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_______
- A. un-supervised learning
- B. Support vector machines
- C. supervised learning
- D. k-Nearest Neighbors
Answer: A
Explanation:
Explanation
The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions
NEW QUESTION 49
Select the correct objectives of principal component analysis
- A. To identify new meaningful underlying variables
- B. Only 1 and 2
- C. All 1, 2 and 3
- D. To reduce the dimensionality of the data set
- E. To discover the dimensionality of the data set
Answer: C
Explanation:
Explanation
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible: and each succeeding component accounts for as much of the remaining variability as possible.
Objectives of principal component analysis
1. To discover or to reduce the dimensionality of the data set.
2. To identify new meaningful underlying variables.
NEW QUESTION 50
In which of the following scenario we can use naTve Bayes theorem for classification
- A. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
- B. To identify whether a fruit is an orange or not based on features like diameter, color and shape
- C. To classify whether an email is spam or not spam
Answer: A,B,C
Explanation:
Explanation
naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters
NEW QUESTION 51
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
- A. Linear Regression
- B. Recommendation system
- C. Hierarchical linear models
- D. Logistic Regression
- E. Maximum likelihood estimation
Answer: D
Explanation:
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
NEW QUESTION 52
In unsupervised learning which statements correctly applies
- A. telling the machine Predict Y for our data X
- B. It does not have a target variable
- C. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Answer: B,C
Explanation:
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?
NEW QUESTION 53
You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?
- A. Include least mutual information with other selected features as a feature selection criterion
- B. Include the number of times each of the words appears in the book in your model
- C. Evaluate a model that only includes the top 100 words
- D. Decrease the size of our training data
Answer: A
Explanation:
Explanation
Correlation measures the linear relationship (Pearson's correlation) or monotonic relationship (Spearman's correlation) between two variables, X and Y.
Mutual information is more general and measures the reduction of uncertainty in Y after observing X.
It is the KL distance between the joint density and the product of the individual densities. So Ml can measure non-monotonic relationships and other more complicated relationships Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Features with high mutual information with the predicted value are good. However a feature may have high mutual information because it is highly correlated with another feature that has already been selected.
Choosing another feature with somewhat less mutual information with the predicted value, but low mutual information with other selected features, may be more beneficial. Hence it may help to also prefer features that are less redundant with other selected features.
NEW QUESTION 54
The method based on principal component analysis (PCA) evaluates the features according to
- A. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
- B. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- C. None of the above
- D. According to the magnitude of the components of the discriminate vector
Answer: A
Explanation:
Explanation
Feature Selection:
The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.
NEW QUESTION 55
......
Databricks-Certified-Professional-Data-Scientist Dumps Ensure Your Passing: https://www.premiumvcedump.com/Databricks/valid-Databricks-Certified-Professional-Data-Scientist-premium-vce-exam-dumps.html