2021 Realistic Verified Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions
Databricks-Certified-Professional-Data-Scientist Real Exam Questions and Answers FREE
Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 63
Support vector machines (SVMs) are a set of supervised learning methods used for
- A. Linear classification
- B. Non-linear classification
- C. Regression
Answer: A,B,C
Explanation:
Explanation
In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.
NEW QUESTION 64
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 two star off of what people really think
- C. It means that your predictions are on average four star off of what people really think
- D. It means that your predictions are on average three star off of what people really think
Answer: A
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. Laplace Smoothing
- C. Linear Regression
- D. Logistic Regression
Answer: B
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
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 67
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?
- A. Linear regression
- B. Decision trees
- C. TF-IDF
- D. Logistic regression
Answer: A
Explanation:
Explanation : A data model explicitly describes a relationship between predictor and response variables.
Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models.
Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation.
If you need to fit data with a nonlinear model, transform the variables to make the relationship linear.
Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.
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NEW QUESTION 68
In unsupervised learning which statements correctly applies
- A. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
- B. telling the machine Predict Y for our data X
- C. It does not have a target variable
Answer: A,C
Explanation:
Explanation
In unsupervised learning we don't have a target variable as we did in
classification and regression.
Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?
NEW QUESTION 69
Which of the following true with regards to the K-Means clustering algorithm?
- A. It discovers the center of each cluster.
- B. Labels are not pre-assigned to each objects in the cluster.
- C. It find each objects fall in which particular cluster
- D. It classify the data based on the labels.
- E. Labels are pre-assigned to each objects in the cluster.
Answer: A,B,C
Explanation:
Explanation
Clustering does not require any predefined labels on the object, rather it consider the attributes on the object.
Hence, option-B is out. Clustering is different than classification technique.
Hence you can discard the option-C as well. It does not use the pre-defined labels, hence it is called unsupervised learning and option-Ais correct. Main purpose of the Clustering technique is to determine the center of each Cluster and then find the distance from that center. If object is near the center than it would fall in that particular cluster. Hence, finally you will have group or clusters created and get to know that objects fall in which particular cluster.
NEW QUESTION 70
What describes a true property of Logistic Regression method?
- A. It works well with discrete variables that have many distinct values.
- B. It handles missing values well.
- C. It is robust with redundant variables and correlated variables.
- D. It works well with variables that affect the outcome in a discontinuous way.
Answer: C
NEW QUESTION 71
Refer to image below
- A. Option D
- B. Option B
- C. Option A
- D. Option C
Answer: C
Explanation:
Explanation
Text Description automatically generated
NEW QUESTION 72
Find out the classifier which assumes independence among all its features?
- A. Naive Bayes
- B. Random forests
- C. Linear Regression
- D. Neural networks
Answer: A
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 73
In which lifecycle stage are appropriate analytical techniques determined?
- A. Model building
- B. Model planning
- C. Data preparation
- D. Discovery
Answer: B
Explanation:
Explanation
In Phase 3, the data science team identifies candidate models to apply to the data for clustering, classifying, or finding relationships in the data depending on the goal of the project, It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became acquainted with the data and understanding the business problems or domain area. These hypotheses help the team frame the analytics to execute in Phase
4 and select the right methods to achieve its objectives.
Some of the activities to consider in this phase include the following: Assess the structure of the datasets. The structure of the datasets is one factor that dictates the tools and analytical techniques for the next phase.
Depending on whether the team plans to analyze textual data or transactional data, for example, different tools and approaches are required.
Ensure that the analytical techniques enable the team to meet the business objectives and accept or reject the working hypotheses. Determine if the situation warrants a single model or a series of techniques as part of a larger analytic workflow. A few example models include association rules and logistic regression Other tools, such as Alpine Miner, enable users to set up a series of steps and analyses and can serve as a front-end user interface (Ul) for manipulating Big Data sources in PostgreSQL.
NEW QUESTION 74
What is the considerable difference between L1 and L2 regularization?
- A. L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
- B. Size of the model can be much smaller in L1 regularization than that produced by L2-regularization
- C. All of the above are correct
- D. L1 regularization has more accuracy of the resulting model
Answer: B
Explanation:
Explanation
The two most common regularization methods are called L1 and L2 regularization. L1 regularization penalizes the weight vector for its L1-norm (i.e. the sum of the absolute values of the weights), whereas L2 regularization uses its L2-norm. There is usually not a considerable difference between the two methods in terms of the accuracy of the resulting model (Gao et al 2007), but L1 regularization has a significant advantage in practice. Because many of the weights of the features become zero as a result of L1-regularized training, the size of the model can be much smaller than that produced by L2-regularization. Compact models require less space on memory and storage, and enable the application to start up quickly. These merits can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
Regularization works by adding the penalty associated with the coefficient values to the error of the hypothesis. This way, an accurate hypothesis with unlikely coefficients would be penalized whila a somewhat less accurate but more conservative hypothesis with low coefficients would not be penalized as much.
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NEW QUESTION 75
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 76
Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:
In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.
Select the correct statement
- A. Precision is low, which means the classifier is predicting positives best
- B. 1 and 3
- C. Precision is low, which means the classifier is predicting positives poorly
- D. 2 and 3
- E. problem domain has a major impact on the measures that should be used to evaluate a classifier within it
Answer: D
Explanation:
Explanation
In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to show that the problem domain has a major impact on the measures that should be used to evaluate a classifier within it, and that looking at the 4 simple cases presented is not sufficient.
NEW QUESTION 77
Which of the following problem you can solve using binomial distribution
- A. It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between
20.03 mm and 20.08 mm - B. A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies
- C. Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
- D. A manufacturer of metal pistons finds that on the average: 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
Answer: D
Explanation:
Explanation
The entire problem can be solved using below method
Binomial: A manufacturer of metal pistons finds that on the average, 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
Poisson: A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies Poisson: Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
Normal: It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20 03 mm and 20.08 mm
NEW QUESTION 78 
The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?
- A. yellow
- B. Neither
- C. blue
Answer: C
Explanation:
Explanation
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected onto any other line, including the yellow one.
NEW QUESTION 79
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?
- A. Try different analytical techniques
- B. Define the process to maintain the model
- C. Transform existing variables
- D. Try different variables
Answer: B
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 80
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