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

Databricks Databricks-Certified-Professional-Data-Scientist Exam

Databricks Certified Professional Data Scientist Exam

Total Questions: 138

Last Updated : 11-11-2024

Get Premium Files

Databricks-Certified-Professional-Data-Scientist Exam - Prepare from Latest, Not Redundant Questions!

Many candidates desire to prepare their Databricks-Certified-Professional-Data-Scientist exam with the help of only updated and relevant study material. But during their research, they usually waste most of their valuable time with information that is either not relevant or outdated. Study4Exam has a fantastic team of subject-matter experts that make sure you always get the most up-to-date preparatory material. Whenever there is a change in the syllabus of the Databricks Certified Professional Data Scientist Exam , our team of experts updates Databricks-Certified-Professional-Data-Scientist questions and eliminates outdated questions. In this way, we save you money and time.

Databricks Databricks-Certified-Professional-Data-Scientist Exam Sample Questions:

Q1.

The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?

Ayellow

Bblue

CNeither
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.
The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected
onto any other line, including the yellow one.

Q2.

What are the advantages of the mutual information over the Pearson correlation for text classification problems?

AThe mutual information has a meaningful test for statistical significance.

BThe mutual information can signal non-linear relationships between the dependent and independent variables.

CThe mutual information is easier to parallelize.

DThe mutual information doesn't assume that the variables are normally distributed.
A linear scaling of the input variables (that may be caused by a change of units for the measurements) is sufficient to modify the PCA results. Feature selection methods that are sufficient for simple distributions of the patterns belonging to different classes can fail in classification tasks with complex decision boundaries. In addition, methods based on a linear dependence (like the correlation) cannot take care of arbitrary relations between the pattern coordinates and the different classes. On the contrary, the mutual information can measure arbitrary relations between variables and it does not depend on transformations acting on the different variables.
This item concerns itself with feature selection for a text classification problem and references mutual information criteria. Mutual information is a bit more sophisticated than just selecting based on the simple correlation of two numbers because it can detect non-linear relationships that will not be identified by the correlation. Whenever possible: mutual information is a better feature selection technique than correlation.
Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much 'knowledge' one can gain of a certain variable by knowing the value of another variable.

Q3.

What describes a true limitation of Logistic Regression method?

AIt does not handle redundant variables well.

BIt does not handle missing values well.

CIt does not handle correlated variables well.

DIt does not have explanatory values.

Q4.

Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

ASemi Linear Regression

BLogistic regression

CNaive Bayesian classification

DLinear regression

EK-means clustering
k-means clustering is a method of vector quantization^ originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.

Q5.

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?

ALogistic Regression

BDecision Trees

CLinear Regression

DARIMA
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.

Solutions:

Question: 1 Answer: B

Question: 2 Answer: C

Question: 3 Answer: B

Question: 4 Answer: E

Question: 5 Answer: B

Disscuss Databricks Databricks-Certified-Professional-Data-Scientist Topics, Questions or Ask Anything Related

Submit Cancel

Currently there are no comments in this discussion, be the first to comment!