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DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Real Exam Questions

Databricks Certified Professional Data Scientist

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Question 1 Single choice

Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation, car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.

Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of 1 3 to each of the three possibilities.

Which of the following method the boss will use to estimate of the probability that Bob drove to work?

  1. A

    Naive Bayes

  2. B

    Linear regression

  3. C

    Random decision forests

  4. D

    None of the above

Show answer and explanation

Correct answer: A

Explanation

Explanation: Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional
probabilities.

Question 2 Single choice

Digit recognition, is an example of.....

  1. A

    Classification

  2. B

    Clustering

  3. C

    Unsupervised learning

  4. D

    None of the above

Show answer and explanation

Correct answer: A

Explanation

Explanation: Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

Question 3 Multiple choice

Select the correct statement which applies to Supervised learning

  1. A

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

  2. B

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

  3. C

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

Show answer and explanation

Correct answers: A, B, C

Explanation

Explanation: 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?

Question 4 Single choice

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 ?

  1. A

    Binomial(X+Y,X+Y)

  2. B

    Poisson(X/Y)

  3. C

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

  4. D

    Poisson(X+Y)

Show answer and explanation

Correct answer: D

Explanation

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).

Question 5 Single choice

Select the correct statement which applies to logistic regression:

  1. A

    Computationally inexpensive, easy to implement knowledge representation easy to interpret

  2. B

    May have low accuracy

  3. C

    Works with Numeric values

  4. D

    Only 1 and 3 are correct

  5. E

    All 1, 2 and 3 are correct

Show answer and explanation

Correct answer: E

Explanation

Explanation: Depending on the size of the data you are uploading, Amazon S3 offers the following options:
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

Question 6 Multiple choice

Which of the following are advantages of the Support Vector machines?

  1. A

    Effective in high dimensional spaces.

  2. B

    it is memory efficient

  3. C

    possible to specify custom kernels

  4. D

    Effective in cases where number of dimensions is greater than the number of samples

  5. E

    Number of features is much greater than the number of samples, the method still give good performances

  6. F

    SVMs directly provide probability estimates

Show answer and explanation

Correct answers: A, B, C, D

Explanation

Explanation: Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are: Effective in high dimensional spaces.
Still effective in cases where number of dimensions is greater than the number of samples.
Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include: If the number of features is much greater than the number of samples, the method is likely to give poor performances.
SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.

Question 7 Single choice

Which of the following technique can be used to the design of recommender systems?

  1. A

    Naive Bayes classifier

  2. B

    Power iteration

  3. C

    Collaborative filtering

  4. D

    1 and 3

  5. E

    2 and 3

Show answer and explanation

Correct answer: C

Explanation

Explanation: One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k- nearest neighbor (k-NN) approach and the Pearson Correlation

Question 8 Single choice

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)?

  1. A

    The lowest cost clustering subject to a stability constraint

  2. B

    The lowest cost clustering

  3. C

    The most stable clustering subject to a minimal cost constraint

  4. D

    The most stable clustering

Show answer and explanation

Correct answer: A

Explanation

Explanation: There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability threshold and select the model that achieves the lowest cost above the stability threshold.

Question 9 Single choice

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?

  1. A

    Logistic Regression

  2. B

    Decision Trees

  3. C

    Linear Regression

  4. D

    ARIMA

Show answer and explanation

Correct answer: B

Explanation

Explanation: A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represents classification rules.
In decision analysis a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.
A decision tree consists of 3 types of nodes:
1. Decision nodes - commonly represented by squares 2. Chance nodes - represented by circles
3. End nodes - represented by triangles
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities.

Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods.

Question 10 Single choice

A data scientist is asked to implement an article recommendation feature for an on-line magazine.

The magazine does not want to use client tracking technologies such as cookies or reading history.
Therefore, only the style and subject matter of the current article is available for making recommendations.
All of the magazine's articles are stored in a database in a format suitable for analytics.

Which method should the data scientist try first?

  1. A

    K Means Clustering

  2. B

    Naive Bayesian

  3. C

    Logistic Regression

  4. D

    Association Rules

Show answer and explanation

Correct answer: A

Explanation

Explanation: kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible.
You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k- means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.