SAS Certified Specialist: Machine Learning Interview Questions

Preparing for an interview is as important as preparing for an exam. Therefore, preparing for an interview takes a lot more practice and confidence to ace any exam. You have to make the best first impression. So to help our candidates to prepare well for the SAS Certified Specialist: Machine Learning interview, we have tried our best to present you with the best and expert-revised interview questions. Candidates should research the company, job roles, and responsibilities, and most importantly look confident while answering any question. Moreover, we have covered all interview questions from basic to intermediate and to advance levels. Therefore, we highly recommend the aspirants prepare with the best and achieve the best. But first, you should be familiar with the basics of what the SAS Certified Specialist: Machine Learning exam is all about.

Overview

This exam is for data scientists who create supervised machine learning models using pipelines in SAS Viya. This exam is administered by SAS and Pearson VUE. Moreover, successful candidates should be familiar with SAS Visual Data Mining and Machine Learning software and be skilled in tasks such as:

• Preparing data and feature engineering
• Creating supervised machine learning models
• Assessing model performance
• Deploying models into production

Now, let’s begin with some of the most important SAS Certified Specialist: Machine Learning interview questions.

Advanced Interview Questions

What is the difference between supervised and unsupervised learning?

Supervised and unsupervised machine learning are two different approaches to machine learning where the algorithms are trained in a different manner.

Supervised Machine Learning:

In supervised machine learning, the algorithms are trained using labeled data. The labeled data consists of input and output variables, where the output variable is known and the algorithm is trained to predict the output variable based on the input variables. The algorithms learn to make predictions based on the patterns in the labeled data, and are then used to make predictions on new, unseen data. The accuracy of the predictions made by the algorithm is evaluated based on how well it matches the known output variables.

Examples of supervised machine learning include linear regression, decision trees, and neural networks.

Unsupervised Machine Learning:

In unsupervised machine learning, the algorithms are trained using unlabeled data. The algorithms are trained to identify patterns in the data and to categorize the data into different groups. The algorithms do not have a target variable to predict, and are instead focused on finding the structure of the data.

Examples of unsupervised machine learning include clustering, principal component analysis, and association rule learning.

In summary, supervised machine learning is focused on making predictions based on labeled data, while unsupervised machine learning is focused on identifying patterns and structures in unlabeled data.

Explain the purpose of normalization in machine learning.

Normalization in machine learning refers to the process of scaling and transforming variables to a standard scale, usually between 0 and 1. This is done to avoid the bias in the model towards variables with a larger scale and to ensure that all variables are on a similar scale. Normalization is significant in machine learning because:

1. It helps improve model convergence and stability.
2. It reduces the effect of large-scale variables on the model, allowing the other features to have a larger impact.
3. It helps eliminate the curse of dimensionality by reducing the impact of features with large values.
4. It helps improve the interpretability of the model by ensuring that all variables have similar ranges.

Overall, normalization is an important step in preprocessing data for machine learning, as it can greatly impact the performance and stability of the model.

What is overfitting in machine learning and how do you prevent it?

Overfitting in machine learning occurs when a model learns the noise in the data instead of the underlying patterns. This leads to high accuracy on the training data but poor performance on unseen data (test data). It occurs when the model is too complex for the given data and is memorizing the training data instead of generalizing it. To prevent overfitting, techniques such as reducing model complexity, adding regularization, and early stopping can be used.

Overfitting occurs when a machine learning model is too complex and fits the training data too closely, resulting in poor generalization to new data. To prevent overfitting, some common techniques are:

1. Simplifying the model: Use a simpler model with fewer parameters to reduce the risk of overfitting.
2. Regularization: Regularization is a technique that adds a penalty term to the loss function to discourage complex models. L1 and L2 regularization are two popular methods.
3. Early stopping: Train the model until the validation error reaches its minimum, then stop to prevent further fitting to the training data.
4. Cross-validation: Divide the data into several folds, train the model on some of the folds and evaluate it on the remaining ones. The average performance across folds gives a better estimate of the model’s generalization performance.
5. Ensemble methods: Use a combination of multiple models to make predictions, such as random forests or gradient boosting, which are less likely to overfit.
6. Using more data: Increasing the amount of training data can also help to prevent overfitting by reducing the relative size of the noise in the data.

What is cross-validation and why is it important in machine learning?

Cross-validation is a technique used to validate the performance of machine learning algorithms by dividing the data into training and testing sets. In cross-validation, the original data set is split into multiple parts, usually into k-folds where k is a positive integer. The first part is used for training the model, and the rest of the parts are used for testing the model. The process is repeated k times, each time using a different part of the data for testing and the remaining parts for training. The results of each iteration are then averaged to determine the overall performance of the model.

Cross-validation is important in machine learning because it provides an unbiased estimate of the model’s generalization performance. In traditional machine learning, we use a single training set and a single testing set to evaluate the performance of the model. However, this method may not provide an accurate estimate of the model’s generalization performance because the testing data may not be representative of the real-world data. Cross-validation solves this problem by using multiple folds of the data to train and test the model, providing a more accurate estimate of the model’s performance.

Moreover, cross-validation also helps in preventing overfitting. Overfitting is a common problem in machine learning where the model becomes too complex and starts fitting the noise in the training data rather than the underlying pattern. This results in a model that performs well on the training data but poorly on the testing data. Cross-validation helps in identifying overfitting by comparing the performance of the model on different folds of the data. If the model’s performance varies greatly between different folds, it is an indication that the model is overfitting.

In conclusion, cross-validation is a crucial step in the development of machine learning algorithms as it provides an unbiased estimate of the model’s performance and helps in identifying overfitting. It is a widely used technique that helps in improving the generalization performance of the model and ensures that the model will perform well on unseen data.

Can you explain the bias-variance tradeoff in machine learning?

The bias-variance tradeoff is a fundamental concept in machine learning that refers to the relationship between the accuracy of a model and its flexibility. A model’s accuracy is determined by the difference between the predictions made by the model and the actual values. The goal of machine learning is to find a model that makes accurate predictions, but there is often a tradeoff between the accuracy of the model and its ability to fit the data.

Bias refers to the systematic error that occurs when a model oversimplifies the relationship between the input and output variables. A model with high bias tends to make the same errors across the entire dataset and is often referred to as underfitting. When a model is underfitting, it is not flexible enough to capture the complexity of the relationship between the input and output variables.

Variance refers to the error that occurs when a model is too flexible and overfits the data. A model with high variance is sensitive to noise in the data and tends to make predictions that are far from the actual values. When a model overfits, it is too flexible and captures the noise in the data, which leads to high variance and low accuracy.

The bias-variance tradeoff is a critical aspect of machine learning because it highlights the tradeoff between model accuracy and flexibility. In order to build an accurate model, it is necessary to balance the bias and variance so that the model is not too simple or too complex. The optimal balance between bias and variance will depend on the particular problem being solved and the nature of the data.

In general, increasing the complexity of a model will reduce bias and increase variance, while decreasing the complexity of the model will increase bias and reduce variance. The goal of machine learning is to find a model that strikes the right balance between bias and variance, resulting in accurate predictions and good performance.

What is the difference between a decision tree and random forest?

A decision tree is a type of machine learning algorithm used for classification or regression problems. It builds a tree-like model of decisions and their possible consequences, with each internal node representing a test on an input feature, each branch representing the outcome of the test, and each leaf node representing a class label or prediction.

A random forest, on the other hand, is an ensemble learning method that combines multiple decision trees to produce a more robust and accurate model. In a random forest, multiple decision trees are trained on different samples of the training data and the outputs of the trees are combined (e.g. through voting) to produce a single output. This helps to reduce overfitting and improve the accuracy of the model. Additionally, the process of training each decision tree on a different sample of the data helps to decorrelate the trees and improve the overall performance of the model.

How do you handle missing data in a dataset for machine learning?

Handling missing data is a critical step in the pre-processing of a dataset for machine learning. Missing data can occur due to various reasons such as faulty data collection methods, data loss, or invalid entries. The presence of missing data in a dataset can affect the performance of a machine learning model, as the model cannot make predictions based on incomplete information.

Here are a few common ways of handling missing data in a dataset:

1. Deletion Method: This method involves removing the entire observation or row that contains missing data. This approach is suitable when the missing data is small in percentage. However, if the missing data is a significant portion of the dataset, deletion may result in loss of information and decrease the accuracy of the model.
2. Imputation Method: This method involves replacing the missing data with a substitute value. The most common imputation method is mean imputation, where missing data is replaced with the mean value of the column. Other imputation methods include median imputation, mode imputation, or regression imputation.
3. Interpolation Method: This method involves estimating missing data based on the values of other variables. For example, linear interpolation can be used to estimate missing data points between two known points.
4. Model-Based Imputation: This method involves using machine learning models to predict missing data based on the values of other variables. For example, Random Forest Regression can be used to predict missing data by training a model on the available data.

In conclusion, the choice of the method used to handle missing data in a dataset will depend on the amount of missing data, the type of data, and the goal of the analysis. It is important to choose the appropriate method to ensure the accuracy and reliability of the machine learning model.

Can you explain gradient descent optimization in machine learning?

Gradient descent optimization is an iterative optimization algorithm used to minimize the cost function in machine learning. The cost function represents the difference between the predicted output and the actual output. The goal of the algorithm is to find the values of the parameters (weights and biases) that result in the minimum cost.

The algorithm starts with initial estimates for the parameters and updates them in the direction of the negative gradient of the cost function with respect to the parameters. The magnitude of the update is determined by the learning rate, which determines how much the parameters are changed in each iteration. The direction of the update is determined by the gradient, which points in the direction of the greatest increase in the cost function.

At each iteration, the parameters are updated using the following formula: w = w – (learning rate * gradient)

where w is the weight, the learning rate is a scalar value that determines the size of the update, and the gradient is the negative derivative of the cost function with respect to the weight.

The optimization process continues until either the cost function converges to a minimum or a stopping criteria is met (such as a maximum number of iterations or a tolerance for the change in the cost function).

Gradient descent optimization is widely used in training neural networks and other machine learning algorithms because it is efficient and can be applied to a wide range of cost functions.

How do you evaluate the performance of a machine learning model?

Evaluating the performance of a machine learning model is a crucial step in the machine learning process, as it helps to determine the accuracy and effectiveness of the model in solving a particular problem. There are several metrics used to evaluate the performance of a machine learning model, including accuracy, precision, recall, F1 score, confusion matrix, ROC curve, and AUC, among others.

1. Accuracy: This is the most commonly used metric to evaluate the performance of a machine learning model. It measures the percentage of correct predictions made by the model. The accuracy score can be calculated by dividing the number of correct predictions by the total number of predictions made by the model.
2. Precision: Precision is a measure of the positive predictions made by the model that are actually true. It is calculated as the number of true positive predictions divided by the number of true positive and false positive predictions.
3. Recall: Recall is a measure of how many of the actual positive observations the model was able to identify. It is calculated as the number of true positive predictions divided by the number of true positive and false negative predictions.
4. F1 score: The F1 score is a harmonic mean of precision and recall, and it is used to measure the balance between precision and recall. A high F1 score indicates that the model has a good balance between precision and recall.
5. Confusion matrix: A confusion matrix is a table that summarizes the performance of a machine learning model by comparing the actual and predicted outcomes. The matrix is typically divided into four quadrants: true positive, false positive, false negative, and true negative.
6. ROC curve: The ROC curve (Receiver Operating Characteristic curve) is a graphical representation of the trade-off between the true positive rate and the false positive rate. The ROC curve plots the true positive rate against the false positive rate at various thresholds. The area under the ROC curve (AUC) is a measure of the model’s accuracy, with a score of 1.0 indicating perfect accuracy.

In conclusion, evaluating the performance of a machine learning model is a crucial step in the machine learning process, as it helps to determine the effectiveness and accuracy of the model in solving a particular problem. By using a combination of the metrics mentioned above, one can obtain a comprehensive understanding of the model’s performance and make informed decisions about future improvements.

Basic Interview Questions

1. How to create a new project?

• Open the project management tool
• Click on the ‘New Project’ button
• Name your project
• Set the project start and end dates
• Assign team members and tasks
• Save and start working on the project

2. How to share a Project?

• Open the project management tool
• Select the project you want to share
• Click on the ‘Share’ button
• Enter the email addresses of the people you want to share the project with
• Choose the access level for each person (view only or edit)
• Send the invitation and wait for the person to accept the invite
• The person can now access and work on the project with you.

3. Explain the Key argument?

In SAS data modification, the KEY argument plays a crucial role in identifying and retrieving observations based on the index values of the data file. The index can either be a simple or composite index, depending on the nature of the SAS data file. It is supplied with the like-named variables from an external source of information.

4. Explain the Keyreset variable?

KEYRESET variable is responsible for initiating the KEY search at the beginning of the index for the data set being read. Its value determines the start position of the lookup process.

5. What happens when the keyreset value is 1 and 0?

If the value is 1, the search begins at the top of the index, whereas if it is 0, the search continues from the previous lookup position.

6. What do you understand by NOBS?

NOBS variable creates a temporary variable that serves as a reference for the total number of observations in the input data set. Its value is typically assigned to the variable for further processing or analysis. Overall, the KEY and NOBS variables are essential components of SAS data modification that ensure efficient retrieval and manipulation of data.

7. Explain the POINT variable?

POINT reads SAS data sets using random (direct) access by observation number. Moreover, the POINT variable is usable anywhere in the DATA step, but it is not added to any SAS data set.

8. When should one use the UNIQUE key?

UNIQUE is used when there are the following duplicate KEY values in the transaction data set so that the quest for a match in the master data set begins at the top of the index file for each duplicate transaction.

9. What is MISSINGCHECK?

MISSINGCHECK prevents missing variable values in a transaction data set from substituting values in a master data set.

10. What is NOMISSINGCHECK?

NOMISSINGCHECK allows missing variable values in a transaction data set to substitute values in a master data set by preventing the check from being performed.

11. What is the use of BY statement in the matching access method?

The matching access method practices the BY statement to match notes from the transaction data set with notes in the master data set. The BY statement specifies a variable that is in the transaction data set and the master data set.

12. Explain the sequential access method?

The sequential access method is the simplest form of the MODIFY statement. It provides less control than the direct access methods. Moreover, with the sequential access method, you can use the NOBS= and END= options to modify a data set.

13. What is the use of Max Class Level?

The Max Class Level property is used to specify the maximum number of class levels that you want to allow. Moreover, if the number of class levels exceeds the specified threshold, the class variable is rejected.

14. What are R and Chi-Square?

When it comes to selecting criteria for a model, both the R-Square and Chi-Square methods are commonly used. However, the decision to apply one over the other often depends on the target measurement level. For example, if the target is an interval target, then only the R-Square criterion will be applied. On the other hand, if the target is a class target, both the R-Square and Chi-Square criteria will be applied to obtain a more comprehensive understanding of the model’s performance.

15. List the steps performed when you apply the R-Square variable selection criterion to a binary target?

• Compute the Squared Correlations
• Forward Stepwise Regression
• Logistic Regression for Binary Targets

The last step is not applied if the target variable is non-binary.

16. What is the use of Bypass Options?

The Bypass Options are used to specify properties about variables that you want to bypass from the variable selection process. Moreover, you can configure whether bypassed variables have a role of input or rejected.

17. List the Variable Selection Node Score Properties?

The following score properties are associated with the Variable Selection node:

• Fistly, Hides Rejected Variables
• Lastly, Hides Unused Variables

18. List some industries that are using Machine learning technologies?

• Financial services
• Government
• Health care
• Retail
• Oil and gas
• Lastly, Transportation

19. List the action sets?

• The partialDependence action
• The linearExplainer action
• Lastly, the shapleyExplainer action

20. What does the partialDependence action do?

The partialDependence action is a powerful tool for calculating partial dependence (PD) and individual conditional expectation (ICE). By analyzing the model’s behavior on a variable-by-variable basis, it provides insights into the impact of each variable on the model’s output.

21. Explain the linearExplainer action?

The local explanations provide information about variable influence and local model behavior for an individual observation, and the global explanations shed light on the overall model behavior by fitting a global surrogate regression model.

22. Explain the shapleyExplainer action?

Another useful tool in the data scientist’s arsenal is the shapleyExplainer action, which is a specialized Shapley value explainer that delivers scalable and accurate Shapley value estimates. By reflecting the contribution of each variable towards the final prediction for an individual observation, the Shapley values offer a comprehensive understanding of how the model works and can be used to identify key features that influence its performance.

23. How to compare models?

• Select one or more models.
• From the Models category view, click  and select Compare
• Click Show Differences.
• Lastly, Click close.

24. How to edit a Test?

• On the Scoring tab of a project, click the Tests tab.
• Click on a test name. The Edit Test window appears.
• Lastly, edit the test properties as needed, and then click Save or Run.

25. List some Classification projects?

• Target variable
• Target event value
• Output event probability variable
• Target level

26. List some Prediction projects?

• Target variable
• Output prediction variable
• Target level

27. What are Macros?

SAS Model Manager is a versatile platform that provides a set of macros designed to help you manage models in your SAS programs. These macros can be used to handle different aspects of the model life cycle, making it easier to organize, maintain, and optimize your models for optimal performance. From managing projects and folders to monitoring and updating models, these macros can help streamline your workflow and improve your productivity as a data scientist.

28. What is Principal Component Analysis?

Principal component analysis (PCA) is an efficient technique that is utilized to decrease the intricacy of large datasets that possess numerous dimensions. This statistical tool is employed to generate a compressed representation of high-dimensional data with fewer dimensions. The essence of PCA lies in its ability to identify the underlying patterns and relationships between the variables.

29. What is the use of the Variables tab?

The Variables tab is used to specify the numerical variables for the analysis. The variables in the Y Variables list correspond to variables in the VAR statement of the PRINCOMP procedure.

30. What is the Plot tab?

Plots tab provides an intuitive visualization of the PCA results by creating various graphs that showcase the data. These plots aid in the identification of potential outliers, clustering of data points, and the significance of each variable in the analysis. In some instances, generating a plot may require additional variables to be added to the data table.

31. List different types of Plots?

• Proportion plot of eigenvalues (scree plot)
• Show cumulative proportions
• Matrix of component score plots
• Correlation pattern plot
• Biplot

32. Explain the Tables Tab?

The tables tab is a useful tool that presents a comprehensive summary of the PCA results. These tables include information on the eigenvalues, eigenvectors, loadings, and explained variances of each component. The eigenvalues and eigenvectors offer insights into the amount of variability captured by each principal component, while the loadings display the correlation between each variable and the principal components. Overall, the Tables tab provides a concise overview of the PCA results, enabling the user to make informed decisions and draw meaningful conclusions.

33. What is a frequency variable?

A frequency variable is a numeric variable whose value represents the frequency of the observation. If you use a frequency variable, the underlying procedure assumes that each observation represents n observations, where n is the value of the frequency variable.

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