Try Free Databricks-Certified-Professional-Data-Scientist Updated Questions & Answers

Question 1

Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?

AIt creates the smaller models

BIt requires the lesser memory to store the coefficients for the model

CIt reduces the non-significant features e.g. punctuations

DNoisy features are removed
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.
The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

Correct answer: B

Question 2

In which of the following scenario you should apply the Bay's Theorem

AThe sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.

BWithin the sample space, there exists an event B, for which P(B) > 0.

CThe analytical goal is to compute a conditional probability of the form: P(Ak | B ).

DIn all above cases

Correct answer: D

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

Correct answer: A

Question 4

Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.

ASupport vector machines

BNaive Bayes

CLogistic regression

DRandom decision forests

EAll of the above
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
* Support vector machines
* Naive Bayes
* Logistic regression
* Random decision forests

Correct answer: E

Question 5

In which of the scenario you can use the linear regression model?

APredicting Home Price based on the location and house area

BPredicting demand of the goods and services based on the weather

CPredicting tumor size reduction based on input as number of radiation treatment

DPredicting sales of the text book based on the number of students in state

Correct answer: A, B, C, D

output variable based on the input variables. In all the cases mentioned in the

question option, you can see that output

can be predicted based on the input variable.

Option-A: Input: Location, House Area and Output: House Price

Option-B : Input: Weather condition, Output: Demand for the goods and services

Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction

Option-D : Input: Number of students and Output: Sale quantity of text book

Databricks-Certified-Professional-Data-Scientist Exam Dumps and Practice Questions

Most Recent Databricks-Certified-Professional-Data-Scientist Exam Dumps

Actual Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions

Unlock all Databricks Databricks-Certified-Professional-Data-Scientist questions