Databricks-Certified-Professional-Data-Scientist Exam Practice Test Instant Access

Question 1

Which of the below best describe the Principal component analysis

ADimensionality reduction

BCollaborative filtering

CClassification

DRegression

EClustering

Answer : A

Question 2

You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use

APCA (Principal component analysis)

BLinear Regression

CLogistic Regression

DClustering
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.

Answer : A

Question 3

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?

ACredit Score

BAge

CIncome

DGender

Answer : A

Question 4

You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

ALinear regression

BLogistic regression

CDecision trees

DTF-IDF

Answer : A

Question 5

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

ADiscovery

BData preparation

CCommunicate Results

DOperationalize
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.

Answer : B

Question 6

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

ADefine the process to maintain the model

BTry different analytical techniques

CTry different variables

DTransform existing variables
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.

Answer : A

Question 7

As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user. So Which recommender system technique benefits most from additional user preference data?

ANaive Bayes classifier

BItem-based collaborative filtering

CLogistic Regression

DContent-based filtering
Item-based scales with the number of items, and user-based scales with the number of users you have. If you have something like a store, you'll have a few thousand items at the most. The biggest stores at the time of writing have around 100,000 items. In the Netflix competition, there were 480,000 users and 17,700 movies. If you have a lot of users: then you'll probably want to go with item-based similarity. For most product-driven recommendation engines, the number of users outnumbers the number of items. There are more people buying items than unique items for sale. Item-based collaborative filtering makes predictions based on users preferences for items. More preference data should be beneficial to this type of algorithm. Content-based filtering recommender systems use information about items or users, and not user preferences, to make recommendations. Logistic Regression, Power iteration and a Naive Bayes classifier are not recommender system techniques.

Answer : B

Databricks Certified Professional Data Scientist Exam Practice Test

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