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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 35
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Expected value
- B. Variance
- C. Linear regression
- D. Quantiles
Answer: C
NEW QUESTION 36
Select the correct option which applies to L2 regularization
- A. No feature selection
- B. Non-sparse outputs
- C. Computational efficient due to having analytical solutions
Answer: A,B,C
Explanation:
The difference between their properties can be promptly summarized as follows:
Table Description automatically generated
NEW QUESTION 37
In which of the following scenario we can use naTve Bayes theorem for classification
- A. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
- B. To classify whether an email is spam or not spam
- C. To identify whether a fruit is an orange or not based on features like diameter, color and shape
Answer: A,B,C
Explanation:
Explanation
naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters
NEW QUESTION 38
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. K Means Clustering
- B. Association Rules
- C. Logistic Regression
- D. Naive Bayesian
Answer: A
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 39
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. Percentage of the customer loan repayment capability
- B. Percentage of the customer should be given loan or not
- C. Probability of the customer default on loan repayment
- D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".
Answer: D
Explanation:
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.
NEW QUESTION 40
Select the correct statement which applies to logistic regression
- A. Only 1 and 3 are correct
- B. Works with Numeric values
- C. May have low accuracy
- D. All 1, 2 and 3 are correct
- E. Computationally inexpensive, easy to implement knowledge representation easy to interpret
Answer: D
Explanation:
Explanation
Depending on the size of the data you are uploading, Amazon S3 offers the following options:
Logistic regression
Pros: Computationally inexpensive, easy to implement knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values^ nominal values
NEW QUESTION 41
Refer to Exhibit
In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?
- A. Linear Regression
- B. Discriminant Analysis
- C. Association Rules
- D. Logistic Regression
Answer: D
NEW QUESTION 42
Refer to the exhibit.
You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of-squares (wss) data as shown in the exhibit.
How many customer groups should you specify?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
NEW QUESTION 43
Which of the following is not a correct application for the Classification?
- A. drug discovery
- B. image recognition
- C. credit scoring
- D. tumor detection
Answer: A
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 44
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. 2/6
- C. 2/3
- D. 1/3
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 45
Clustering is a type of unsupervised learning with the following goals
- A. 1 and 2
- B. Find similarities in the training data
- C. Maximize a utility function
- D. Not to maximize a utility function
- E. 2 and 3
Answer: E
Explanation:
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.
NEW QUESTION 46
Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?
- A. Choose the initial centroids so that they all He along different axes
- B. Only consider values of k larger than log(N), where N is the number of observations in the data set
- C. Choose the initial centroids so that they are far away from each other
- D. Run at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set
Answer: C
Explanation:
Explanation
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes This Question-is about the properties that make k-means an effective clustering heuristic which primarily deal with ensuring that the initial centers are far away from each other. This is how modern k-means algorithms like k-means++ guarantee that with high probability Lloyd's algorithm will find a clustering within a constant factor of the optimal possible clustering for each k.
NEW QUESTION 47
Select the correct algorithm of unsupervised algorithm
- A. K-Means
- B. K-Nearest Neighbors
- C. Support Vector Machines
- D. Naive Bayes
Answer: B
Explanation:
Explanation
Sup Supervised learning tasks
Classification Regression
k-Nearest Neighbors Linear
Naive Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso
Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window
NEW QUESTION 48
In which phase of the analytic lifecycle would you expect to spend most of the project time?
- A. Discovery
- B. Communicate Results
- C. Operationalize
- D. Data preparation
Answer: D
Explanation:
Explanation
In the data preparation phase of the Data Analytics Lifecycle, the data range and distribution can be obtained.
If the data is skewed, viewing the logarithm of the data (if it's all positive) can help detect structures that might otherwise be overlooked in a graph with a regular, nonlogarithmic scale.
When preparing the data, one should look for signs of dirty data, as explained in the previous section. Examining if the data is unimodal or multimodal will give an idea of how many distinct populations with different behavior patterns might be mixed into the overall population. Many modeling techniques assume that the data follows a normal distribution. Therefore, it is important to know if the available dataset can match that assumption before applying any of those modeling techniques.
NEW QUESTION 49
In which of the scenario you can use the regression to predict the values
- A. All 1 ,2 and 3
- B. Probability of the celebrity divorce
- C. Mobile companies can use it to forecast manufacturing defects
- D. Samsung can use it for mobile sales forecast
- E. Only 1 and 2
Answer: A
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 50
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)?
- A. The lowest cost clustering
- B. The lowest cost clustering subject to a stability constraint
- C. The most stable clustering subject to a minimal cost constraint
- D. The most stable clustering
Answer: B
Explanation:
Explanation
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.
NEW QUESTION 51
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
- A. Discovery
- B. Communicate Results
- C. Data Preparation
- D. Model Building
Answer: C
NEW QUESTION 52
You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use
- A. Linear Regression
- B. PCA (Principal component analysis)
- C. Logistic Regression
- D. Clustering
Answer: B
Explanation:
Explanation
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.
NEW QUESTION 53
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 54
Select the statement which applies correctly to the Naive Bayes
- A. Works with a small amount of data
- B. Sensitive to how the input data is prepared
- C. Works with nominal values
Answer: A,B,C
NEW QUESTION 55
Find out the classifier which assumes independence among all its features?
- A. Linear Regression
- B. Neural networks
- C. Random forests
- D. Naive Bayes
Answer: D
Explanation:
Explanation
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.
NEW QUESTION 56
Question-13. Which of the following is not the Classification algorithm?
- A. None of the above
- B. Logistic Regression
- C. Hidden Markov Models
- D. Neural Network
- E. Support Vector Machine
Answer: A
Explanation:
Explanation
Logistic regression
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.
Support Vector Machines
As with naive Bayes, Support Vector Machines (or SVMs) can be used to solve the task of assigning objects to classes. But the way this task is solved is completely different to the setting in naive Bayes.
Neural Network
Neural Networks are a means for classifying multidimensional objects.
Hidden Markov Models
Hidden Markov Models are used in multiple areas of machine learning, such as speech recognition, handwritten letter recognition, or natural language processing.
NEW QUESTION 57
Select the correct problems which can be solved using SVMs
- A. Hand-written characters can be recognized using SVM
- B. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
- C. SVMs are helpful in text and hypertext categorization
- D. Classification of images can also be performed using SVMs
Answer: A,B,C,D
Explanation:
Explanation
SVMs can be used to solve various real world problems:
* SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
* Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
* SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
* Hand-written characters can be recognized using SVM
NEW QUESTION 58
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. Create clusters based on the data and use them as model inputs
- B. Force all 15 variables into the model as independent variables
- C. Break variables A, B, and C into their own univariate models
- D. Create interaction variables based only on variables A, B, and C
Answer: A
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 59
Which of the following skills a data scientists required?
- A. Should possess good programming skills
- B. He should possess database administrative skills.
- C. Should be very good at mathematics and statistic
- D. He should be creative
- E. Web designing to represent best visuals of its results from algorithm.
Answer: A,C,D
Explanation:
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
NEW QUESTION 60
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