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Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam Practice Tests 2021 | RealValidExam
NEW QUESTION 37
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 most stable clustering subject to a minimal cost constraint
- B. The lowest cost clustering subject to a stability constraint
- C. The lowest cost clustering
- 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 38
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 39
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: C
NEW QUESTION 40
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 identify whether a fruit is an orange or not based on features like diameter, color and shape
- C. To classify whether an email is spam or not spam
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 41
What are the advantages of the Hashing Features?
- A. Requires the less memory
- B. Easily reverse engineer vectors to determine which original feature mapped to a vector location
- C. Less pass through the training data
Answer: A,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 42
You are having 1000 patients' data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?
- A. You will be converting each height value to centimeters
- B. You will be taking square root of height
- C. You will be adding height with the numeric value 100
- D. You will be dividing both age and height with their respective standard deviation
Answer: A,D
Explanation:
Explanation
When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100.
While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum.
However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.
NEW QUESTION 43
A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.
Above is an example of
- A. Recommendation system
- B. Maximum likelihood estimation
- C. Linear Regression
- D. Logistic Regression
- E. Hierarchical linear models
Answer: D
Explanation:
Explanation
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 44
You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?
- A. You will randomly reduce the number of variables
- B. You cannot discard any variable for creating clusters.
- C. You can combine several variables in one variable
- D. You will find the correlation among the variables and from their variables are not co-related will be discarded.
- E. You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.
Answer: C,E
Explanation:
Explanation
When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation.
You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.
NEW QUESTION 45
Select the correct option from the below
- A. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.
- B. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.
- C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or
+_to -_, then you need to look unsupervised learning - D. If you're not trying to predict a target value, then you need to look into unsupervised learning
- E. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.
Answer: A,B,D,E
Explanation:
Explanation
If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to
999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.
NEW QUESTION 46
Select the correct option which applies to L2 regularization
- A. Computational efficient due to having analytical solutions
- B. No feature selection
- C. Non-sparse outputs
Answer: A,B,C
Explanation:
The difference between their properties can be promptly summarized as follows:
Table Description automatically generated
NEW QUESTION 47
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. Logistic Regression
- B. Clustering
- C. PCA (Principal component analysis)
- D. Linear Regression
Answer: C
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 48
Which of the following question statement falls under data science category?
- A. How many products have been sold in a last month?
- B. Which is the optimal scenario for selling this product?
- C. What happens, if these scenario continues?
- D. Where is a problem for sales?
- E. What happened in last six months?
Answer: B,C
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 49
Select the correct problems which can be solved using SVMs
- A. Classification of images can also be performed using SVMs
- B. Hand-written characters can be recognized using SVM
- C. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
- D. SVMs are helpful in text and hypertext categorization
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 50
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 51
Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.
- A. All of the above
- B. Support vector machines
- C. Random decision forests
- D. Naive Bayes
- E. Logistic regression
Answer: A
Explanation:
Explanation
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
NEW QUESTION 52
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
- A. P(E1,E2,E3)P(E2)P(E3)
- B. P(E1,E2|E3)P(E3)
- C. P(E1,E2,E3)P(E1)/P(E2:E3)
- D. P(E1,E2;E3)/P(E2,E3)
- E. P(E1,E2|E3)P(E2|E3)P(E3)
Answer: D
Explanation:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so P(E1|E2) = P(E1.E2)/P(E2) P(E1,E2,E3)/P(E2,E3) If the events are A and B respectively, this is said to be "the probability of A given B" It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.
NEW QUESTION 53
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