[Sep-2021] Pass Databricks-Certified-Professional-Data-Scientist Exam in First Attempt UpdatedDatabricks-Certified-Professional-Data-Scientist Pass4Leader Exam Question
Databricks Certification Dumps Databricks-Certified-Professional-Data-Scientist Exam for Full Questions - Exam Study Guide
NEW QUESTION 52
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. Supervised
- B. Unsupervised
- C. None of the above
- 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 53
Let's say you have two cases as below for the movie ratings
1. You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars
2. You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?
- A. In both cases, the contribution to the RMSE, could varies
- B. In both cases, the contribution to the RMSE is the same
- C. None of the above
- D. In both cases, the contribution to the RMSE is the different
Answer: B
NEW QUESTION 54
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?
- A. Define the process to maintain the model
- B. Transform existing variables
- C. Try different variables
- D. Try different analytical techniques
Answer: A
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 55
Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct
- A. It means that your predictions are on average one star off of what people really think
- B. It means that your predictions are on average three star off of what people really think
- C. It means that your predictions are on average two star off of what people really think
- D. It means that your predictions are on average four star off of what people really think
Answer: A
NEW QUESTION 56
You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?
- A. Logistic Regression
- B. Naive Bayes
- C. Random Decision Forests
- D. Any one of the above
Answer: D
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like texts, corrections, 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
NEW QUESTION 57
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. 1/6
- B. 2/3
- C. 1/3
- D. 2/6
Answer: A
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 58
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Variance
- B. Expected value
- C. Quantiles
- D. Linear regression
Answer: D
NEW QUESTION 59
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?
- A. Noisy features are removed
- B. It reduces the non-significant features e.g. punctuations
- C. It creates the smaller models
- D. It requires the lesser memory to store the coefficients for the model
Answer: D
Explanation:
Explanation
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.
Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.
The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
NEW QUESTION 60
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(B|A,C)
- B. P(A,B|C) P(B|C) =P(A|C,B)
- C. P(A,B|C) P(B|C) =P(C|B,C)
- D. P(A,B|C) P(B|C) =P(A|B,C)
Answer: D
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 61
Refer to exhibit
You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1.
Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C.
The results of the regression are seen in the exhibit. You cannot request additional data. what is a way that you could try to increase the R2 of the model without artificially inflating it?
- A. Force all 15 variables into the model as independent variables
- B. Break variables A, B, and C into their own univariate models
- C. Create clusters based on the data and use them as model inputs
- D. Create interaction variables based only on variables A, B, and C
Answer: C
Explanation:
Explanation
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X.
The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression^ where multiple correlated dependent variables are predicted, rather than a single scalar variable.) In linear regression data are modeled using linear predictor functions, and unknown model parameters are estimated from the data.
Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X.
Less commonly: linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X.
Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X:
which is the domain of multivariate analysis.
NEW QUESTION 62
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 works well with variables that affect the outcome in a discontinuous way.
- D. It is robust with redundant variables and correlated variables.
Answer: D
NEW QUESTION 63
Select the correct statement which applies to K-Nearest Neighbors
- A. Require less memory
- B. No Assumption about the data
- C. Computationally expensive
- D. Works with Numeric Values
Answer: B,C,D
Explanation:
Explanation : k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
NEW QUESTION 64
Refer to image below
- A. Option A
- B. Option D
- C. Option B
- D. Option C
Answer: A
Explanation:
Explanation
Text Description automatically generated
NEW QUESTION 65
In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because
- A. The normalizing constant is always very close to 1
- B. The normalizing constant only has a small impact on the maximum likelihood
- C. The normalizing constant doesn't impact the maximizing value
- D. The normalizing constant is often zero and can cause division by zero
Answer: C
Explanation:
Explanation
(Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.
NEW QUESTION 66
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_______
- A. k-Nearest Neighbors
- B. Support vector machines
- C. un-supervised learning
- D. supervised learning
Answer: C
Explanation:
Explanation
The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions
NEW QUESTION 67
In which of the scenario you can use the linear regression model?
- A. Predicting demand of the goods and services based on the weather
- B. Predicting sales of the text book based on the number of students in state
- C. Predicting tumor size reduction based on input as number of radiation treatment
- D. Predicting Home Price based on the location and house area
Answer: A,B,C,D
Explanation:
Explanation : You can use the linear regression model for predicting the continuous output variable based on the input variables. In all the cases mentioned in the question option, you can see that output can be predicted based on the input variable.
Option-A: Input: Location, House Area and Output: House Price
Option-B : Input: Weather condition, Output: Demand for the goods and services Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction Option-D : Input: Number of students and Output: Sale quantity of text book
NEW QUESTION 68
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. R-squared never decreases upon adding more independent variables.
- C. When R square =1 , all the residuals are equal to 0
- D. When R square =0, all the residual are equal to 1
Answer: A,B,C
NEW QUESTION 69
Which of the below best describe the Principal component analysis
- A. Collaborative filtering
- B. Clustering
- C. Classification
- D. Regression
- E. Dimensionality reduction
Answer: E
NEW QUESTION 70
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?
- A. Logistic Regression
- B. Linear Regression
- C. ARIMA
- D. Decision Trees
Answer: D
Explanation:
Explanation
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.
NEW QUESTION 71
In which of the scenario you can use the regression to predict the values
- A. Samsung can use it for mobile sales forecast
- B. Probability of the celebrity divorce
- C. All 1 ,2 and 3
- D. Only 1 and 2
- E. Mobile companies can use it to forecast manufacturing defects
Answer: C
Explanation:
Explanation
Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.
NEW QUESTION 72
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. L1 gives Non-sparse output while L2 gives sparse outputs
- B. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
- C. None of the above
- 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:
A picture containing text Description automatically generated
NEW QUESTION 73
A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?
- A. Logistic Regression
- B. K Means Clustering
- C. Naive Bayesian
- D. Association Rules
Answer: B
Explanation:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
NEW QUESTION 74
Which of the following is not a correct application for the Classification?
- A. credit scoring
- B. image recognition
- C. drug discovery
- D. tumor detection
Answer: C
Explanation:
Explanation
Classification : Build models to classify data into different categories credit scoring, tumor detection, image recognition Regression: Build models to predict continuous data, electricity load forecasting, algorithmic trading, drug discovery
NEW QUESTION 75
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