Databricks Certified Professional Data Scientist Exam Questions Instant Access

Question 1

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)?

AThe lowest cost clustering subject to a stability constraint

BThe lowest cost clustering

CThe most stable clustering subject to a minimal cost constraint

DThe most stable clustering
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.

Answer : A

Question 2

Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

AThe data is unformatted.

BThere is not enough data to create a test set.

CThere are missing values in the data.

DThere are categorical variables in the model.

Answer : B

Question 3

Which of the following are point estimation methods?

AMAP

BMLE

CMMSE
Point estimators
* minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
* best linear unbiased estimator (BLUE)
* minimum mean squared error (MMSE)
* median-unbiased estimator, minimizes the risk of the absolute-error loss function
* maximum likelihood (ML)
* method of moments, generalized method of moments

Answer : A, B, C

Question 4

A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

ALinear regression

BCollaborative filtering

CNaive Bayes

DIdentification Test
In this problem you have been given high-dimensional independent variables like yes, no: 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

Answer : C

Question 5

Select the correct option which applies to L2 regularization

AComputational efficient due to having analytical solutions

BNon-sparse outputs

CNo feature selection

Answer : A, B, C

The difference between their properties can be promptly summarized as follows:

Question 6

You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?

AAssociation Rules

BNaive Bayes Classifier

CK-means Clustering

DLinear Regression

EGrouping
Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.

Answer : C

Question 7

You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc. During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?

AData Preparation

BDiscovery

COperationalize

DModel planning

EModel building
To answer whether the model you are building is robust enough or not you need to
have answer below questions at least
- Model is performing as expected with the test data or not?
- Whatever hypothesis defined in the initial phase is being tested or not?
- Do we need more data?
- Domain experts are convinced or not with the model?
And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.

Answer : E