Databricks-Certified-Professional-Data-Scientist Pre-Exam Practice Tests | (Updated 140 Questions)
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NEW QUESTION 73
The method based on principal component analysis (PCA) evaluates the features according to
- A. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- B. According to the magnitude of the components of the discriminate vector
- C. None of the above
- D. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
Answer: D
Explanation:
Explanation
Feature Selection:
The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.
NEW QUESTION 74
Which of the following is a Continuous Probability Distributions?
- A. Negative binomial distribution
- B. Normal probability distribution
- C. Binomial probability distribution
- D. Poisson probability distribution
Answer: B
NEW QUESTION 75
What is the considerable difference between L1 and L2 regularization?
- A. L1 regularization has more accuracy of the resulting model
- B. L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
- C. All of the above are correct
- D. Size of the model can be much smaller in L1 regularization than that produced by L2-regularization
Answer: D
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 76
Select the correct objectives of principal component analysis
- A. All 1, 2 and 3
- B. To identify new meaningful underlying variables
- C. To discover the dimensionality of the data set
- D. To reduce the dimensionality of the data set
- E. Only 1 and 2
Answer: A
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.
Objectives of principal component analysis
1. To discover or to reduce the dimensionality of the data set.
2. To identify new meaningful underlying variables.
NEW QUESTION 77
Which of the following are point estimation methods?
- A. MMSE
- B. MLE
- C. MAP
Answer: A,B,C
Explanation:
Explanation
Point estimators
* minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
* best linear unbiased estimator (BLUE)
* minimum mean squared error (MMSE)
* median-unbiased estimator, minimizes the risk of the absolute-error loss function
* maximum likelihood (ML)
* method of moments, generalized method of moments
NEW QUESTION 78
Select the correct statement which applies to K-Nearest Neighbors
- A. Require less memory
- B. No Assumption about the data
- C. Computationally expensive
- D. Works with Numeric Values
Answer: B,C,D
Explanation:
Explanation : k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
NEW QUESTION 79
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 80
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. Logistic Regression
- B. Laplace Smoothing
- C. Linear Regression
- D. Naive Bayes
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 81
Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?
- A. Linear regression
- B. K-means clustering
- C. Logistic regression
- D. Naive Bayesian classification
- E. Semi Linear Regression
Answer: B
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.
The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.
NEW QUESTION 82
Select the correct statement which applies to logistic regression
- A. May have low accuracy
- B. Works with Numeric values
- C. Computationally inexpensive, easy to implement knowledge representation easy to interpret
Answer: A,B,C
NEW QUESTION 83
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 84
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. Logistic Regression
- B. Collaborative filtering
- C. Naive Bayes classifier
- D. Content-based filtering
Answer: B
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 85
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. User-based collaborative filtering
- B. Content-based filtering
- C. Item-based collaborative filtering
- D. Naive Bayes classifier
Answer: B
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 86
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. Decision trees
- B. TF-IDF
- C. Logistic regression
- D. Linear regression
Answer: D
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 87
Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?
- A. Cluster Density
- B. Support Vector Count
- C. Sum of Absolute Errors
- D. Mean Absolute Error
Answer: D
Explanation:
Explanation
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references.
Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.
Support vector count and cluster density do not apply to recommender systems.
MAE and AUC are both valid and useful metrics for measuring recommender systems.
NEW QUESTION 88
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