Latest [Jun 16, 2022] 100% Passing Guarantee - Brilliant Databricks-Certified-Professional-Data-Scientist Exam Questions PDF [Q23-Q40]

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Latest [Jun 16, 2022] 100% Passing Guarantee – Brilliant Databricks-Certified-Professional-Data-Scientist Exam Questions PDF

Databricks-Certified-Professional-Data-Scientist Certification – Valid Exam Dumps Questions Study Guide! (Updated 140 Questions)

Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:

Topic	Details
Topic 1	Specific algorithms like ALS for recommendation and isolation forests for outlier detection Logging and model organization with MLflow
Topic 2	A complete understanding of the basics of machine learning in-sample vs. out-of sample data
Topic 3	A intermediate understanding of the steps in the machine learning lifecycle Model training, selection, and production
Topic 4	A complete understanding of the basics of machine learning model management Linear, logistic, and regularized regression

NO.23 Which of the following skills a data scientists required?

Web designing to represent best visuals of its results from algorithm.

He should be creative

Should possess good programming skills

Should be very good at mathematics and statistic

He should possess database administrative skills.

NO.24 Assume some output variable “y” is a linear combination of some independent input variables “A” plus some independent noise “e”. The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?

Option A

Option B

Option C

Option D

NO.25 Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because

The penalty term is not differentiate

The second derivative is not constant

The objective function is not convex

The constraints are quadratic

NO.26 Which method is used to solve for coefficients bO, b1, … bn in your linear regression model:

Apriori Algorithm

Ridge and Lasso

Ordinary Least squares

Integer programming

NO.27 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?

It creates the smaller models

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

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

Noisy features are removed

NO.28 Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

Semi Linear Regression

Logistic regression

Naive Bayesian classification

Linear regression

K-means clustering

NO.29 Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be……

Numerical

Categorical

Both 1 and 2 are correct

None of the 1 and 2 are correct

NO.30 A fruit may be considered to be an apple if it is red, round, and about 3″ in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

Presence of the other features.

Absence of the other features.

Presence or absence of the other features

None of the above

NO.31 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?

Data Preparation

Discovery

Operationalize

Model planning

Model building

NO.32 Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?

Only consider values of k larger than log(N), where N is the number of observations in the data set

Run at least log(N) iterations of Lloyd’s algorithm, where N is the number of observations in the data set

Choose the initial centroids so that they all He along different axes

Choose the initial centroids so that they are far away from each other

NO.33 Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn’t rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie’s wedding?

Naive Bayes

Logistic Regression

Random Decision Forests

All of the above

NO.34 Question-34. Stories appear in the front page of Digg as they are “voted up” (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members. Which of the following technique is used to make such recommendation engine?

Naive Bayes classifier

Collaborative filtering

Logistic Regression

Content-based filtering

NO.35 Select the correct statement which applies to logistic regression

Computationally inexpensive, easy to implement knowledge representation easy to interpret

May have low accuracy

Works with Numeric values

Only 1 and 3 are correct

All 1, 2 and 3 are correct

NO.36 What are the advantages of the Hashing Features?

Requires the less memory

Less pass through the training data

Easily reverse engineer vectors to determine which original feature mapped to a vector location

NO.37 What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

Expected value

Variance

Linear regression

Quantiles

NO.38 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?

Linear regression

Collaborative filtering

Naive Bayes

Identification Test

NO.39 Reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions. It is done in_______

supervised learning

un-supervised learning

k-Nearest Neighbors

Support vector machines

NO.40 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?

Association Rules

Naive Bayes Classifier

K-means Clustering

Linear Regression

Grouping