Databricks Databricks Certifications DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 91:

  You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also

  highly co-related.

  What is the best solution for that?

  A. You will take cube root of height

  B. You will take square root of weight

  C. You will take square of the height.

  D. You would consider using BMI (Body Mass Index)

  Correct Answer: D

  Explanation: If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)

• Question 92:

  Which of the following skills a data scientists required?

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

  B. He should be creative

  C. Should possess good programming skills

  D. Should be very good at mathematics and statistic

  E. He should possess database administrative skills.

  Correct Answer: BCD

  Explanation: Yes a data scientists should have combination of skills like to solve the complex problem he should be creative as well as able to find new solutions and use of existing data. And solve the problem skills required are programming as currently we see SAS, R: Python, Spark, Java and SPSS even day by day new technologies are coming. To apply various existing and new algorithm using Machine Learning, or Al it require good mathematics and statistics skills (Where the programmer feels, weaknesses). Another skill required is using visualization techniques like Qlik, Tableau etc

• Question 93:

  In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

  A. Discovery

  B. Data Preparation

  C. Model Building

  D. Communicate Results

  Correct Answer: B

• Question 94:

  In which lifecycle stage are test and training data sets created?

  A. Model planning

  B. Discovery

  C. Model building

  D. Data preparation

  Correct Answer: C

  Explanation: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data. Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data Model planning: Phase 3 is model planning, where the team determines the methods, techniques, and workflow it intends to follow for the subsequent model building phase. The team explores the data to learn about the relationships between variables and subsequently selects key variables and the most suitable models. Model building: In Phase 4, the team develops datasets for testing, training, and production purposes. In addition, in this phase the team builds and executes models based on the work done in the model planning phase. The team also considers whether its existing tools will suffice for running the models, or if it will need a more robust environment for executing models and workflows (for example, fast hardware and parallel processing, if applicable). Communicate results: In Phase 5, the team, in collaboration with major stakeholders, determines if the results of the project are a success or a failure based on the criteria developed in Phase 1. The team should identify key findings, quantify the business value, and develop a narrative to summarize and convey findings to stakeholders. Operationalize: In Phase 6, the team delivers final reports, briefings, code, and technical documents. In addition, the team may run a pilot project to implement the models in a production environment.

• Question 95:

  Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response. Which of the following method you should not use to identify whether the response is valid or not?

  A. Naive Bayes

  B. Logistic Regression

  C. Random Decision Forests

  D. Any one of the above

  Correct Answer: D

  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

• Question 96:

  What describes a true property of Logistic Regression method?

  A. It handles missing values well.

  B. It works well with discrete variables that have many distinct values.

  C. It is robust with redundant variables and correlated variables.

  D. It works well with variables that affect the outcome in a discontinuous way.

  Correct Answer: C

• Question 97:

  What describes a true limitation of Logistic Regression method?

  A. It does not handle redundant variables well.

  B. It does not handle missing values well.

  C. It does not handle correlated variables well.

  D. It does not have explanatory values.

  Correct Answer: B

• Question 98:

  If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

  A. P(E1)/P(E2)

  B. P(E1+E2)/P(E1)

  C. P(E2)/P(E1)

  D. P(E2)/(P(E1+E2)

  Correct Answer: C

• Question 99:

  You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process?

  A. Probability of the customer default on loan repayment

  B. Percentage of the customer loan repayment capability

  C. Percentage of the customer should be given loan or not

  D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".

  Correct Answer: D

  Explanation: Classification is the process of using several inputs to produce one or more outputs. For example the input might be the income, education and current debt of a customer The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.

• Question 100:

  Clustering is a type of unsupervised learning with the following goals

  A. Maximize a utility function

  B. Find similarities in the training data

  C. Not to maximize a utility function

  D. 1 and 2

  E. 2 and 3

  Correct Answer: E

  Explanation: type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data. The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.