[Jan 22, 2022] Valid Databricks-Certified-Professional-Data-Scientist Test Answers & Databricks-Certified-Professional-Data-Scientist Exam PDF
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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 62
Which of the following statement is true for the R square value in the regression model?
- A. R square can be increased by adding more variables to the model.
- B. When R square =0, all the residual are equal to 1
- C. When R square =1 , all the residuals are equal to 0
- D. R-squared never decreases upon adding more independent variables.
Answer: A,C,D
Explanation:
Explanation
R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.
NEW QUESTION 63
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?
- A. Credit Score
- B. Gender
- C. Income
- D. Age
Answer: A
NEW QUESTION 64
In which of the following scenario you should apply the Bay's Theorem
- A. In all above cases
- B. Within the sample space, there exists an event B, for which P(B) > 0.
- C. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
- D. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
Answer: A
NEW QUESTION 65
Support vector machines (SVMs) are a set of supervised learning methods used for
- A. Linear classification
- B. Non-linear classification
- C. Regression
Answer: A,B,C
Explanation:
Explanation
In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.
NEW QUESTION 66
In which lifecycle stage are appropriate analytical techniques determined?
- A. Model building
- B. Model planning
- C. Data preparation
- D. Discovery
Answer: B
Explanation:
Explanation
In Phase 3, the data science team identifies candidate models to apply to the data for clustering, classifying, or finding relationships in the data depending on the goal of the project, It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became acquainted with the data and understanding the business problems or domain area. These hypotheses help the team frame the analytics to execute in Phase
4 and select the right methods to achieve its objectives.
Some of the activities to consider in this phase include the following: Assess the structure of the datasets. The structure of the datasets is one factor that dictates the tools and analytical techniques for the next phase.
Depending on whether the team plans to analyze textual data or transactional data, for example, different tools and approaches are required.
Ensure that the analytical techniques enable the team to meet the business objectives and accept or reject the working hypotheses. Determine if the situation warrants a single model or a series of techniques as part of a larger analytic workflow. A few example models include association rules and logistic regression Other tools, such as Alpine Miner, enable users to set up a series of steps and analyses and can serve as a front-end user interface (Ul) for manipulating Big Data sources in PostgreSQL.
NEW QUESTION 67
What are the advantages of the Hashing Features?
- A. Easily reverse engineer vectors to determine which original feature mapped to a vector location
- B. Less pass through the training data
- C. Requires the less memory
Answer: B,C
Explanation:
Explanation
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or word-like data.
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.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
NEW QUESTION 68
Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,
- A. P(A,B|C) P(B|C) =P(C|B,C)
- B. P(A,B|C) P(B|C) =P(A|B,C)
- C. P(A,B|C) P(B|C) =P(B|A,C)
- D. P(A,B|C) P(B|C) =P(A|C,B)
Answer: B
Explanation:
Explanation
From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C) P(B,C) =P(A|BC) This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)
NEW QUESTION 69
You are using one approach for the classification where to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success, where agents might be rewarded for doing certain actions and punished for doing others. Which kind of this learning
- A. None of the above
- B. Unsupervised
- C. Supervised
- D. Regression
Answer: B
Explanation:
Explanation
Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do! The approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished fordoing others.
NEW QUESTION 70
Spam filtering of the emails is an example of
- A. 1 and 3 are correct
- B. Clustering
- C. Unsupervised learning
- D. Supervised learning
- E. 2 and 3 are correct
Answer: D
Explanation:
Explanation
Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier:
e-mails marked as spam or ham.
NEW QUESTION 71
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...
- A. None of the above
- B. L1 gives Non-sparse output while L2 gives sparse outputs
- C. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
- D. L2 is the sum of the square of the weights, while L1 is just the sum of the weights
Answer: D
Explanation:
Explanation
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:
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NEW QUESTION 72
Which of the following question statement falls under data science category?
- A. Where is a problem for sales?
- B. Which is the optimal scenario for selling this product?
- C. What happened in last six months?
- D. What happens, if these scenario continues?
- E. How many products have been sold in a last month?
Answer: B,D
Explanation:
Explanation
This question wants to check your understanding about Bl and Data Science. Bl was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in Bl or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc.
But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.
NEW QUESTION 73
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?
- A. Model building
- B. Operationalize
- C. Model planning
- D. Data Preparation
- E. Discovery
Answer: A
Explanation:
Explanation
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.
NEW QUESTION 74
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).
- A. 2/3
- B. 1/6
- C. 1/3
- D. 2/6
Answer: B
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION 75
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......
- A. Categorical
- B. Numerical
- C. Both 1 and 2 are correct
- D. None of the 1 and 2 are correct
Answer: C
Explanation:
Explanation
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.
NEW QUESTION 76
You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?
- A. Naive Bayes
- B. All of the above
- C. Laplace Smoothing
- D. Logistic Regression
Answer: C
Explanation:
Explanation
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.
NEW QUESTION 77
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?
- A. Try different analytical techniques
- B. Define the process to maintain the model
- C. Transform existing variables
- D. Try different variables
Answer: B
Explanation:
Explanation
Operationalize In the final phase, the team communicates the benefits of the project more broadly and sets up a pilot project to deploy the work in a controlled way before broadening the work to a full enterprise or ecosystem of users. In Phase 4. the team scored the model in the analytics sandbox.
NEW QUESTION 78
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