Course 2022 Databricks-Certified-Professional-Data-Scientist Test Prep Training Practice Exam Download
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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 61
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 62
In which of the scenario you can use the regression to predict the values
- A. Probability of the celebrity divorce
- B. Only 1 and 2
- C. All 1 ,2 and 3
- D. Samsung can use it for mobile sales forecast
- 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 63
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 64
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. Logistic Regression
- C. All of the above
- D. Laplace Smoothing
Answer: D
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 65
Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.
- A. Naive Bayes
- B. Logistic Regression
- C. Linear Regression
- D. Laplace Smoothing
Answer: D
Explanation:
Explanation
Smooth the estimates: consider flipping a coin for which the probability of heads is p, where p is unknown, and our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is that it avoids estimating any probabilities to be zero, even for events never observed in the data.
Laplace add-one smoothing now assigns too much probability to unseen words
NEW QUESTION 66
You are creating a model for the recommending the book at Amazon.com, so which of the following recommender system you will use you don't have cold start problem?
- A. Naive Bayes classifier
- B. User-based collaborative filtering
- C. Content-based filtering
- D. Item-based collaborative filtering
Answer: C
Explanation:
Explanation
The cold start problem is most prevalent in recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). In the content-based approach, the system must be capable of matching the characteristics of an item against relevant features in the user's profile. In order to do this, it must first construct a sufficiently-detailed model of the user's tastes and preferences through preference elicitation. This may be done either explicitly (by querying the user) or implicitly (by observing the user's behaviour). In both cases, the cold start problem would imply that the user has to dedicate an amount of effort using the system in its 'dumb' state - contributing to the construction of their user profile - before the system can start providing any intelligent recommendations.
Content-based filtering recommender systems use information about items or users to make recommendations, rather than user preferences, so it will perform well with little user preference data. Item-based and user-based collaborative filtering makes predictions based on users' preferences for items, os they will typically perform poorly with little user preference data. Logistic regression is not recommender system technique.
NEW QUESTION 67
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 often zero and can cause division by zero
- B. The normalizing constant doesn't impact the maximizing value
- C. The normalizing constant is always very close to 1
- D. The normalizing constant only has a small impact on the maximum likelihood
Answer: B
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 68
Which of the following question statement falls under data science category?
- A. Which is the optimal scenario for selling this product?
- B. How many products have been sold in a last month?
- C. What happened in last six months?
- D. What happens, if these scenario continues?
- E. Where is a problem for sales?
Answer: A,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 69
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 most stable clustering
- C. The lowest cost clustering
- D. The lowest cost clustering subject to a stability constraint
Answer: D
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 70
You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?
- A. Root Mean Square Error
- B. Both land 2
- C. Information is not good enough.
- D. Sum of Errors
- E. Mean Absolute Error
Answer: C
NEW QUESTION 71
What describes a true property of Logistic Regression method?
- A. It works well with variables that affect the outcome in a discontinuous way.
- B. It is robust with redundant variables and correlated variables.
- C. It handles missing values well.
- D. It works well with discrete variables that have many distinct values.
Answer: B
NEW QUESTION 72
Feature Hashing approach is "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" now with large vectors or with multiple locations per feature in Feature hashing?
- A. Is a problem with accuracy as well as hard to understand what classifier us doing
- B. It is hard to understand what classifier is doing
- C. It is easy to understand what classifier is doing
- D. Is a problem with accuracy
Answer: B
Explanation:
Explanation
FEATURE HASHING
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 73
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(A|B,C)
- B. P(A,B|C) P(B|C) =P(B|A,C)
- C. P(A,B|C) P(B|C) =P(A|C,B)
- D. P(A,B|C) P(B|C) =P(C|B,C)
Answer: A
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 74
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?
- A. Define the process to maintain the model
- B. Try different variables
- C. Transform existing 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 75
Select the correct statement which applies to logistic regression
- A. Works with Numeric values
- B. Only 1 and 3 are correct
- C. All 1, 2 and 3 are correct
- D. May have low accuracy
- E. Computationally inexpensive, easy to implement knowledge representation easy to interpret
Answer: C
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 76
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Ordinary Least squares
- B. Ridge and Lasso
- C. Integer programming
- D. Apriori Algorithm
Answer: A
Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters
NEW QUESTION 77
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. Classification of images can also be performed using SVMs
- 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 78
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