Real Databricks-Certified-Professional-Data-Scientist dumps - Real Databricks dumps PDF in here [Dec-2021]
Realistic Exam4Labs Databricks-Certified-Professional-Data-Scientist Dumps PDF - 100% Passing Guarantee
Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
| Topic 6 |
|
| Topic 7 |
|
NEW QUESTION 33
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 34
As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user. So Which recommender system technique benefits most from additional user preference data?
- A. Naive Bayes classifier
- B. Item-based collaborative filtering
- C. Logistic Regression
- D. Content-based filtering
Answer: B
Explanation:
Explanation
Item-based scales with the number of items, and user-based scales with the number of users you have. If you have something like a store, you'll have a few thousand items at the most. The biggest stores at the time of writing have around 100,000 items. In the Netflix competition, there were 480,000 users and 17,700 movies. If you have a lot of users: then you'll probably want to go with item-based similarity. For most product-driven recommendation engines, the number of users outnumbers the number of items. There are more people buying items than unique items for sale. Item-based collaborative filtering makes predictions based on users preferences for items. More preference data should be beneficial to this type of algorithm. Content-based filtering recommender systems use information about items or users, and not user preferences, to make recommendations. Logistic Regression, Power iteration and a Naive Bayes classifier are not recommender system techniques.
NEW QUESTION 35
Select the correct statement regarding the naive Bayes classification
- A. Independent variables can be assumed
- B. it only requires a small amount of training data to estimate the parameters
- C. only the variances of the variables for each class need to be determined
- D. for each class entire covariance matrix need to be determined
Answer: A,B,C
Explanation:
Explanation
An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.
NEW QUESTION 36
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(E2|E3)P(E3)
- D. P(E1,E2;E3)/P(E2,E3)
- E. P(E1,E2,E3)P(E1)/P(E2: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 37
You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer
- A. Differentiation
- B. Generalization
- C. Transformation
- D. Integration
Answer: D
Explanation:
Explanation
Text Description automatically generated
Text Description automatically generated
Text Description automatically generated
NEW QUESTION 38
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Expected value
- B. Quantiles
- C. Linear regression
- D. Variance
Answer: C
Explanation:
Explanation
Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.
NEW QUESTION 39
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 40
You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user. What kind of this recommendation engine is ?
- A. Collaborative filtering
- B. Naive Bayes classifier
- C. Logistic Regression
- D. Content-based filtering
Answer: A
Explanation:
Explanation
Another aspect of collaborative filtering systems is the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and help the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user
NEW QUESTION 41
You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?
- A. You will take cube root of height
- B. You will take square of the height.
- C. You will take square root of weight
- D. You would consider using BMI (Body Mass Index)
Answer: D
Explanation:
Explanation
If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)
NEW QUESTION 42
In which of the following scenario we can use naTve Bayes theorem for classification
- A. To identify whether a fruit is an orange or not based on features like diameter, color and shape
- B. To classify whether an email is spam or not spam
- C. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
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 43
Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow.
Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)
- A. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
- B. marginal probability that P(H=Not Hit) is the sum of the H= Hit row
- C. The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.
Answer: A,C
Explanation:
Explanation
The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green. Similarly, the marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
NEW QUESTION 44
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. It reduces the non-significant features e.g. punctuations
- B. Noisy features are removed
- 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 45
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 can combine several variables in one variable
- B. You cannot discard any variable for creating clusters.
- C. You will randomly reduce the number of variables
- 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: A,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 46
Clustering is a type of unsupervised learning with the following goals
- A. Maximize a utility function
- B. 1 and 2
- C. 2 and 3
- D. Not to maximize a utility function
- E. Find similarities in the training data
Answer: C
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 47
RMSE measures error of a predicted
- A. Categorical values
- B. For booth Numerical and categorical values
- C. Numerical Value
Answer: C
NEW QUESTION 48
Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B.Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B.40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?
- A. None of the above
- B. Bays Theorem
- C. Poisson Distribution
- D. Binomial Distribution
Answer: B
NEW QUESTION 49
Digit recognition, is an example of.....
- A. Clustering
- B. Classification
- C. None of the above
- D. Unsupervised learning
Answer: B
Explanation:
Explanation
Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.
NEW QUESTION 50
A problem statement is given as below
Hospital records show that of patients suffering from a certain disease, 75% die of it. What is the probability that of 6 randomly selected patients, 4 will recover?
Which of the following model will you use to solve it.
- A. Poisson
- B. Any of the above
- C. Binomial
- D. Normal
Answer: C
NEW QUESTION 51
......
Verified Databricks-Certified-Professional-Data-Scientist dumps Q&As Latest Databricks-Certified-Professional-Data-Scientist Download: https://www.exam4labs.com/Databricks-Certified-Professional-Data-Scientist-practice-torrent.html