Exam DP-100: Designing and Implementing a Data Science Solution on Azure Interview Questions

The Designing and Implementing a Data Science Solution on Azure Examination is for Azure Data Scientist who applies their knowledge of data science and machine learning for implementing and running machine learning workloads on Azure. Moreover, this exam DP-100 requires planning and developing a suitable working environment for data science workloads on Azure and running data experiments, and training predictive models. The candidate appearing for this examination should develop knowledge about machine learning. Further, as a data scientist, you will be training, evaluating, and deploying models to build AI solutions that satisfy business objectives. To help you in your interview preparation, we have curated a number of questions.

Advanced Interview Questions

Can you explain the differences between supervised and unsupervised learning?

Supervised learning and unsupervised learning are two types of machine learning techniques.

Supervised learning is a type of machine learning where the model is trained on labeled data. In other words, the data used to train the model includes both input features (also known as independent variables) and corresponding output labels (also known as dependent variables). The goal of supervised learning is to predict the output labels for new, unseen data based on the patterns learned from the labeled training data. Examples of supervised learning algorithms include linear regression, logistic regression, decision trees, and support vector machines.

Unsupervised learning, on the other hand, is a type of machine learning where the model is trained on unlabeled data. In other words, the data used to train the model includes only the input features (independent variables) without any corresponding output labels (dependent variables). The goal of unsupervised learning is to discover patterns or relationships in the data without any prior knowledge of the output labels. Examples of unsupervised learning algorithms include clustering, dimensionality reduction, and anomaly detection.

In summary, supervised learning is used when we have labeled data and we want to predict the output based on the input features, while unsupervised learning is used when we only have input features and we want to discover patterns in the data.

How do you handle missing data in a dataset?

Handling missing data in a dataset can be a challenging task, as it can affect the accuracy and reliability of the results. In SAP Business Objects Web Intelligence, there are several techniques that you can use to handle missing data, such as:

1. Deleting rows: You can delete the rows that contain missing data, but this can lead to a loss of information and bias the results.
2. Replacing missing data: You can replace the missing data with a default value, such as the mean, median, or mode of the data. But this approach can also introduce bias, especially if the missing data is not missing at random.
3. Interpolation: You can use interpolation techniques to estimate the missing data based on the surrounding data points.
4. Imputation: You can use imputation techniques to estimate the missing data based on the pattern of the data.
5. Excluding missing data from the analysis: You can exclude the missing data from the analysis, but this can also lead to a loss of information.
6. Using a flag for missing data: You can create a new variable that indicates the presence of missing data and use this variable in the analysis.

Ultimately, the best approach will depend on the specific dataset and the research question.

Can you explain how to implement a decision tree in Azure Machine Learning?

To implement a decision tree in Azure Machine Learning, you can use the Azure Machine Learning SDK for Python. Here is an example of how to do this:

1. Start by installing the Azure Machine Learning SDK by running !pip install azureml-sdk[notebooks,automl] in your Jupyter notebook.
2. Next, import the necessary libraries:

from azureml.core import Workspace

from azureml.core.dataset import Dataset

from azureml.train.automl import AutoMLConfig

3. Connect to your Azure Machine Learning workspace by providing your subscription ID, resource group, and workspace name:

ws = Workspace.from_config()

4. Next, create an AutoMLConfig object with the necessary settings, such as the task type (classification or regression), the primary metric to optimize, and the path to your training data:

automl_config = AutoMLConfig(task=’classification’,

primary_metric=’AUC_weighted’,

                                                         max_time_sec=3600,

                                                        n_cross_validations=5,

                                                       path=’./’)

5. Now you can use the fit() method to train the decision tree model on your data:

from azureml.core.experiment import Experiment

experiment=Experiment(ws, “automl_classification”)

local_run = experiment.submit(automl_config, show_output=True)

6. Once the training is completed, you can retrieve the best model:

best_run, fitted_model = local_run.get_output()

7. Finally, you can use the ‘fitted_model’ object to make predictions on new data.

How do you select the appropriate algorithm for a given dataset and problem?

Selecting the appropriate algorithm for a given dataset and problem is an important step in the data analysis process. The following are some general guidelines that you can use to select the appropriate algorithm for a given dataset and problem in Web Intelligence:

1. Understand the problem: Before selecting an algorithm, it’s important to understand the problem that you are trying to solve, and the type of data that you are working with.
2. Identify the type of problem: Depending on the problem, you may need to use a supervised or unsupervised algorithm. Supervised algorithms are used for problems that involve prediction, such as classification or regression. Unsupervised algorithms are used for problems that involve discovery, such as clustering or dimensionality reduction.
3. Consider the size and complexity of the dataset: Depending on the size and complexity of the dataset, you may need to use an algorithm that can handle large and complex datasets, such as Random Forests or Neural Networks.
4. Consider the computational resources available: Some algorithms require more computational resources than others, so it’s important to consider the resources that are available when selecting an algorithm.
5. Consider the interpretability of the algorithm: Some algorithms are more interpretable than others, and it’s important to consider the interpretability of the algorithm if you need to explain the results to non-technical stakeholders.
6. Test the algorithm: Once you have selected an algorithm, it’s important to test

Can you explain the concept of “feature engineering” and its importance in a data science project?

Feature engineering is the process of transforming raw data into features that can be used in a machine-learning model. It is an important step in the data science process, as the quality and characteristics of the features used in a model can greatly impact its performance.

During feature engineering, data scientists will select and transform variables in the dataset to create new features that will improve the model’s performance. This can include combining multiple variables into a single feature, creating new variables based on mathematical operations, or converting categorical variables into numerical variables.

For example, if a dataset contains the variables “age” and “income,” a data scientist may create a new feature called “age-income ratio” to represent the ratio of a person’s income to their age.

In addition to creating new features, feature engineering also involves selecting a subset of features from the dataset to use in the model. This is called feature selection. Feature selection is important because it can help to improve the model’s performance by removing irrelevant or redundant features, which can improve the accuracy and reduce the complexity of the model.

Overall, feature engineering is an important aspect of data science projects, as it allows data scientists to optimize the performance of their models by creating relevant and informative features that better represent the underlying patterns in the data.

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

There are several ways to evaluate the performance of a machine learning model, depending on the type of problem and the nature of the data. Some common methods include:

1. Accuracy: This is one of the most commonly used metrics for classification problems, it measures the proportion of correctly classified instances. It is calculated as the number of correct predictions divided by the total number of instances.
2. Confusion matrix: A confusion matrix is a table that is often used to describe the performance of a classification algorithm. It shows the number of true positives, true negatives, false positives, and false negatives. A confusion matrix can be useful for understanding the errors made by a model and identifying the types of errors that are most costly.
3. Precision: Precision is a metric that calculates the proportion of true positive predictions among all positive predictions. It is calculated as the number of true positives divided by the sum of the true positives and false positives.
4. Recall: Recall is a metric that calculates the proportion of true positive predictions among all actual positive instances. It is calculated as the number of true positives divided by the sum of the true positives and false negatives.
5. F1 Score: F1 Score is the harmonic mean of precision and recall. It is used to balance the trade-off between precision and recall and is calculated as 2*((precision*recall)/(precision+recall))
6. ROC Curve and AUC: ROC (Receiver Operating Characteristic) curve is a graphical representation of the performance of a binary classifier, AUC (Area Under the Curve) is a single number summary of classifier performance, it ranges from 0 to 1, where 1 means perfect classifier and 0 means worst classifier.
7. Mean Squared Error (MSE): MSE is a commonly used metric for regression problems. It calculates the average of the squared differences between the predicted values and the true values.
8. Mean Absolute Error (MAE): MAE is another commonly used metric for regression problems. It calculates the average of the absolute differences between the predicted values and the true values.

Can you explain how to deploy a machine-learning model in Azure?

Yes, to deploy a machine learning model in Azure, you can use the Azure Machine Learning SDK for Python. Here is an example of how to do this:

1. Start by installing the Azure Machine Learning SDK by running !pip install azureml-sdk[notebooks,automl] in your Jupyter notebook.
2. Next, import the necessary libraries:

from azureml.core import Workspace, Model

from azureml.core.model import InferenceConfig

from azureml.core.webservice import AciWebservice, Webservice

3. Connect to your Azure Machine Learning workspace by providing your subscription ID, resource group, and workspace name:

ws = Workspace.from_config()

4. Next, register the model to your workspace so that it can be deployed:

model = Model.register(model_path = “model.pkl”, # this should be the path to your model file

                       model_name = “model_name”,

                       tags = {‘type’: “classification”},

                       description = “example model”,

                       workspace = ws)

5. Create an InferenceConfig object, which will specify the runtime environment for the deployed model and the scoring script used to make predictions

inference_config = InferenceConfig(runtime= “python”,

entry_script=”score.py”, # this should be the path to your scoring script

                                   conda_file=”myenv.yml”) 

# this should be the path to your conda environment file

6. Create the deployment configuration and specify the number of CPU cores and gigabyte of RAM needed by the deployed model

deployment_config = 

AciWebservice.deploy_configuration(cpu_cores=1, 

memory_gb=1)

7. Finally, you can deploy the model as a web service using the following code:

service = Model.deploy(ws, “myservice”, [model], 

inference_config, deployment_config)

8. Once the deployment is completed, you can test the deployed service using the following command:

service.run(input_data)

Note: Depending on the size of the model, it may take a while to deploy the model on the service.

How do you handle imbalanced classes in a dataset?

There are several techniques that can be used to handle imbalanced classes in a dataset, including:

1. Undersampling: This technique involves randomly removing instances from the majority class to balance the class distribution. This can be done by removing instances randomly or by using a technique called Tomek links, which removes the instances that are closest to the instances of the minority class.
2. Oversampling: This technique involves generating new instances of the minority class. This can be done by duplicating existing instances, or by using techniques like SMOTE (Synthetic Minority Over-sampling Technique) which creates synthetic instances of the minority class by interpolating between existing instances.
3. Cost-sensitive learning: This technique involves modifying the machine learning algorithm to take into account the imbalance in the class distribution. For example, by increasing the weight of the minority class or by using different evaluation metrics.
4. Ensemble methods: Ensemble methods like Bagging and Boosting combine multiple models to improve performance. Bagging methods are based on random subsets of the data and Boosting methods that are based on weighted subsets of the data.
5. Data augmentation: This technique involves creating new samples from the existing data, for example by applying different noise, rotation, scaling, or flipping on images.
6. Penalized learning: Some machine learning algorithms are able to penalize the misclassification of the minority class, which helps the model pay more attention to the minority class.
7. Change the threshold of the decision function: Changing the threshold of the decision function of the classifier can help to balance the precision and recall, it can be done by adjusting the probability threshold for classification and adjusting the decision threshold for the classifier.

It is important to note that no single approach is guaranteed to work best for all datasets and it is often beneficial to try a combination of techniques to find the best solution. It is also important to evaluate the performance of the model using different performance metrics, such as precision, recall, and F1-score, in order to get a more comprehensive understanding of the model’s performance on the imbalanced data.

Can you explain the concept of “regularization” in machine learning?

In machine learning, regularization is a technique that is used to prevent overfitting and improve the generalization performance of a model. Overfitting occurs when a model is trained too well on the training data, and as a result, it performs poorly on new, unseen data.

Regularization works by adding a penalty term to the cost function that the model is trying to optimize. This penalty term, also known as a regularization term, discourages the model from assigning too much weight to any one feature, thus reducing the complexity of the model. The idea behind regularization is to balance the fit of the model to the training data with the complexity of the model.

There are two main types of regularization:

1. L1 regularization (also known as Lasso regularization): L1 regularization adds a penalty term to the cost function that is proportional to the absolute value of the weights. This results in some weights becoming exactly zero, effectively removing those features from the model. This type of regularization is useful for feature selection.
2. L2 regularization (also known as Ridge regularization): L2 regularization adds a penalty term to the cost function that is proportional to the square of the weights. This results in small, non-zero weights, effectively reducing the impact of each feature on the model. This type of regularization is useful for reducing overfitting by keeping the weights small.

It’s important to note that regularization is used as a way to prevent overfitting and improve the generalization performance of the model. It is not always the best option for all problems, it depends on the specific problem and dataset.

How do you ensure the security and privacy of data in an Azure data science solution?

Ensuring the security and privacy of data in an Azure data science solution involves several steps and best practices, including:

1. Access controls: Use Azure Active Directory (AAD) to authenticate and authorize access to data, resources, and services in your Azure data science solution. You can use role-based access control (RBAC) to assign different levels of permissions to different users and groups.
2. Network security: Use Azure virtual networks and network security groups to control inbound and outbound network traffic and isolate resources within your data science solution. Use Azure ExpressRoute to create private connections to Azure, and use Azure VPN to connect to on-premises resources.
3. Data encryption: Use Azure Key Vault to store and manage the encryption keys used to encrypt data at rest and in transit. Use Azure Disk Encryption to encrypt virtual machine disks, and use Azure SQL TDE to encrypt Azure SQL databases.
4. Compliance: Use Azure Policy to ensure that your data science solution adheres to compliance requirements such as HIPAA, SOC2, and PCI-DSS. Azure Policy provides a built-in set of policies to help you achieve compliance and can also be used to define custom policies to fit your specific needs.
5. Data governance: Use Azure Policy to define and enforce policies for data governance such as data retention and archiving, data lineage and data masking. Azure Data Factory can be used to manage data lineage and data masking
6. Data protection: Use Azure Advanced Threat Protection to detect and respond to advanced threats, Azure Information Protection to classify and protect sensitive data, and Azure Security Center to monitor and protect your data science solution.
7. Azure Purview can be used for data discovery and data governance across different data silos.
8. Azure Data Catalog can be used for data discovery and data governance across different data silos and also helps to understand the data lineage

These are just a few examples of the many security and privacy features and services that are available in Azure to help protect your data science solution. It’s important to understand the specific security and privacy requirements of your organization and to design and implement a solution that meets those requirements.

Basic Interview Questions

What do you mean by Microsoft Azure ?

Azure is a cloud computing platform created by Microsoft. It’s a highly adaptable cloud platform for development, service hosting, data storage, and service management.

How can you define machine learning ?

Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and improve on their own without having to be explicitly programmed. Machine learning is concerned with the creation of computer programmes that can access data and learn on their own.

What is NameNode ?

It serves as the hub of HDFS. It keeps track of diverse documents across groups and maintains information on HDFS. The genuine data isn’t kept on this server. DataNodes are where the data is kept.

Define Data Engineering.

In the field of big data, the term “data engineering” is used. It is based on the collection and analysis of data. The data compiled from various sources is simply insufficient. Data engineering aids in the transformation of this immature data into useful information.

What do you mean by Data Modelling ?

Data modelling is a method of storing sophisticated programming structures as a graph that everyone can comprehend. It’s a fair representation of data exceptions that are linked to various information items and concepts.

Describe each component of a Hadoop application.

• Hadoop Common: This is a standard set of tools and libraries that Hadoop use.
• HDFS: This Hadoop application corresponds to the document framework that houses Hadoop data. It’s a flexible document framework with a high data transfer rate.

• Hadoop MapReduce is based on a data preparation method for large-scale data.
• Within the Hadoop group, Hadoop YARN is utilised to manage assets. Client task scheduling can be done with it.

Define the term “Hadoop Streaming.”

It is a utility that considers the creation of the guide, decreases employment, and assigns them to a certain group.

What exactly is a NameNode?

It serves as the hub of HDFS. It keeps track of diverse documents across groups and maintains information on HDFS. The genuine data isn’t kept on this server. DataNodes are where the data is kept.

What does HDFS stand for in its entire form?

HDFS stands for Hadoop Distributed File System in its entire form.

Define Blocks and Block Scanner in HDFS.

Data files are divided into blocks, which are the smallest units. Hadoop divides numerous files into small chunks by default. The Block Scanner examines the list of blocks added to a DataNode.

What is the purpose of Azure Active Directory?

Azure Active Directory is a system for managing identity and access. It’s utilised to give your employees access to certain products and services on your network. Salesforce.com, Twitter, and other sites are examples. Azure Active Directory includes some built-in support for applications in its gallery that may be include directly.

What is autoscaling in Azure?

Auto-scaling is a way to automatically scale up or down the number of computing resources that are being to your application based on its needs at any given time.

When Block Scanner discovers a faulty data block, what happens next?

When Block Scanner finds a corrupted data square, the following sequence of events occurs:

• Firstly, DataNode informs NameNode when Block Scanner finds a faulty data square.
• Secondly, NameNode begins the process of creating a duplicate of the corrupted block.
• Thirdly, The replication factor is attempted to be coordinated by the replication score of the right replicas.

What are the two messages that DataNode sends to NameNode?

NameNode receives two messages from DataNode: one is a message from DataNode, and the other is a message from DataNode. The first is a block report, while the second is a heartbeat report.

List out different XML arrangement records in Hadoop?

There are five XML arrangement records in Hadoop:

• Mapred-site
• Core-site
• HDFS-site
• Yarn-site

Describe the key features of Hadoop.

The following are some of Hadoop’s most notable features:

• Firstly, It’s a freeware-friendly framework that’s open-source.
• Secondly, Hadoop works well with a wide range of hardware and makes it simple to add new hardware to a cluster.
• Last but not least, It keeps the information in a separate group from the rest of the activities.

What are four V’s of big data?

Four V’s of big data are:

• Velocity
• Variety
• Volume
• Veracity

What is the form of COSHH?

The form of COSHH is Classification and Optimization-based Schedule for Heterogeneous Hadoop System.

Explain the concept of Star Schema.

The simplest data warehouse system is the star schema or star join schema. It’s called a star pattern because its structure resembles that of a star. The star’s focal point may have one reality table and several related dimension tables in star construction. This method is used to question large data sets.

How do you put a big data solution in place?

The steps below demonstrate how to set up a big data solution:

• Firstly, Integrate data from many sources such as RDBMS, SAP, MySQL, and Salesforce.
• Secondly, Save the data in either a NoSQL database or HDFS.
• Thirdly, Use processing architectures like Pig, Spark, and MapReduce to deploy big data solution systems.

What is the full type of YARN?

The full type of YARN is Yet Another Resource Negotiator.

 List different modes in Hadoop.

Modes in Hadoop are:

• Firstly, Standalone mode
• Secondly, Pseudo distributed mode
• Thirdly, Fully distributed mode.

Describe the basic responsibilities of an information engineer.

The source arrangement of information is dealt with by information engineers. They also prevent information redundancy by rearranging complex information structures. They frequently provide ELT and information modification as well.

In Hadoop, how can you achieve security?

The steps to achieving security in Hadoop are as follows:

• The first step is to double-check the client’s verification channel with the server. Give the customer the time-stamped receipt.
• In the second step, the customer uses the time-stamped receipt to request a service ticket from TGS.

What happens when the maximum number of failed tries to authenticate yourself with Azure AD is reached?

To lock accounts, we employ a more advanced technique. This is determine by the request’s IP address and the passwords submitted. The length of the lockout is also determine by the likelihood of an attack.

What is Azure App Service, and how does it work?

Azure App Service is a fully manage Platform-as-a-Service (PaaS) offering for experience developers that provides a comprehensive set of capabilities for web, mobile, and integration scenarios. Mobile apps in Azure App Service provide Enterprise Developers and System Integrators with a highly adaptable and universally accessible mobile application development platform that provides a broad range of capabilities to mobile engineers.

We have covered all the important questions for a Designing and Implementing a Data Science Solution on Azure examination interview. You can also, try out free practice test and get the best of it. It will also, help you in giving a better understanding of the examination. You can also, check Azure Machine Learning online training for further knowledge.