[Sep 03, 2024] DSA-C02 Sample with Accurate & Updated Questions
DSA-C02 Exam Info and Free Practice Test | Real4Prep
NEW QUESTION # 35
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
- A. Publish listings for datasets that can be customized for the consumer.
- B. Share live datasets securely and in real-time without creating copies of the data or im-posing data integration tasks on the consumer.
- C. Publish listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer base.
- D. Eliminate the costs of building and maintaining APIs and data pipelines to deliver data to customers.
Answer: A,B,C,D
Explanation:
Explanation
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer.
With Paid Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government.
Support for Microsoft Azure Government is planned.
NEW QUESTION # 36
Which one is the incorrect option to share data in Snowflake?
- A. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
- B. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
- C. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.
- D. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
Answer: B
Explanation:
Explanation
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
NEW QUESTION # 37
Which one is incorrect understanding about Providers of Direct share?
- A. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
- B. As a data provider, you share a database with one or more Snowflake accounts.
- C. You can create as many shares as you want, and add as many accounts to a share as you want.
- D. If you want to provide a share to many accounts, you can do the same via Direct Share.
Answer: D
Explanation:
Explanation
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.
NEW QUESTION # 38
Which ones are the type of visualization used for Data exploration in Data Science?
- A. Sand Visualization
- B. 2D-Density Plots
- C. Heat Maps
- D. Newton AI
- E. Feature Distribution by Class
Answer: A,B,C
Explanation:
Explanation
Type of visualization used for exploration:
Correlation heatmap
Class distributions by feature
Two-Dimensional density plots.
All the visualizations are interactive, as is standard for Plotly.
For More details, please refer the below link:
https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41
NEW QUESTION # 39
Which command is used to install Jupyter Notebook?
- A. pip install jupyter-notebook
- B. pip install notebook
- C. pip install nbconvert
- D. pip install jupyter
Answer: D
Explanation:
Explanation
Jupyter Notebook is a web-based interactive computational environment.
The command used to install Jupyter Notebook is pip install jupyter.
The command used to start Jupyter Notebook is jupyter notebook.
NEW QUESTION # 40
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the aggregate method shown in below code do?
g = df.groupby(df.index.str.len())
g.aggregate({'A':len, 'B':np.sum})
- A. Computes length of column A and Sum of Column B values of each group
- B. Computes length of column A
- C. Computes Sum of column A values
- D. Computes length of column A and Sum of Column B values
Answer: A
Explanation:
Explanation
Computes length of column A and Sum of Column B values of each group
NEW QUESTION # 41
Select the correct mappings:
I. W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter II. K in the K-Nearest Neighbour algorithm --> Model Hyperparameter III. Learning rate for training a neural network --> Model Hyperparameter IV. Batch Size --> Model Parameter
- A. I,II,III
- B. III,IV
- C. I,II
- D. II,III,IV
Answer: A
Explanation:
Explanation
Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting the learning process of the model.
What are hyperparameters?
In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process." Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.
Some examples of Hyperparameters in Machine Learning
The k in kNN or K-Nearest Neighbour algorithm
Learning rate for training a neural network
Train-test split ratio
Batch Size
Number of Epochs
Branches in Decision Tree
Number of clusters in Clustering Algorithm
Model Parameters:
Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:
They are used by the model for making predictions.
They are learned by the model from the data itself
These are usually not set manually.
These are the part of the model and key to a machine learning Algorithm.
Model Hyperparameters:
Hyperparameters are those parameters that are explicitly defined by the user to control the learning process.
Some key points for model parameters are as follows:
These are usually defined manually by the machine learning engineer.
One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error.
Some examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.
NEW QUESTION # 42
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
- A. by intercept
- B. no change
- C. by 1
- D. by its slope
Answer: D
Explanation:
Explanation
What is linear regression?
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatoryvariable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
For linear regression Y=a+bx+error.
If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.
For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.
NEW QUESTION # 43
Which metric is not used for evaluating classification models?
- A. Mean absolute error
- B. Recall
- C. Precision
- D. Accuracy
Answer: A
Explanation:
Explanation
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.
NEW QUESTION # 44
Which of the following process best covers all of the following characteristics?
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates,functional dependencies, embedded value dependencies, and performing inter-table analysis.
- A. Data Collection
- B. Data Visualization
- C. Data Virtualization
- D. Data Profiling
Answer: D
Explanation:
Explanation
Data processing and analysis cannot happen without data profiling-reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.
What is data profiling?
Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.
Data profiling is a crucial part of:
Data warehouse and business intelligence (DW/BI) projects-dataprofiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.
Data conversion and migration projects-data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.
Source system data quality projects-data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).
Data profiling involves:
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.
NEW QUESTION # 45
Which of the following metrics are used to evaluate classification models?
- A. F1 score
- B. Area under the ROC curve
- C. Confusion matrix
- D. All of the above
Answer: D
Explanation:
Explanation
Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification and regression. Some metrics, like precision-recall, are useful for multiple tasks. Classification and regression are examples of supervised learning, which constitutes a majority of machine learning applications. Using different metrics for performance evaluation, we should be able to im-prove our model's overall predictive power before we roll it out for production on unseen data. Without doing a proper evaluation of the Machine Learning model by using different evaluation metrics, and only depending on accuracy, can lead to a problemwhen the respective model is deployed on unseen data and may end in poor predictions.
Classification metrics are evaluation measures used to assess the performance of a classification model.
Common metrics include accuracy (proportion of correct predictions), precision (true positives over total predicted positives), recall (true positives over total actual positives), F1 score (har-monic mean of precision and recall), and area under the receiver operating characteristic curve (AUC-ROC).
Confusion Matrix
Confusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes. It is a table with combinations of predicted and actual values.
It is extremely useful for measuring the Recall, Precision, Accuracy, and AUC-ROC curves.
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
These metrics help assess the classifier's effectiveness in correctly classifying instances of different classes.
Understanding how well a machine learning model will perform on unseen data is the main purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced then other methods like ROC/AUC perform better in evaluating the model performance.
ROC curve isn't just a single number but it's a whole curve that provides nuanced details about the behavior of the classifier. It is also hard to quickly compare many ROC curves to each other.
NEW QUESTION # 46
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
- A. numpy plots
- B. sklearn plots
- C. FULL dependence plots (FDP)
- D. Partial dependence plots(PDP)
Answer: D
Explanation:
Explanation
Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.
NEW QUESTION # 47
Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?
- A. Holdout method
- B. Leave-one-out cross-validation
- C. k-fold cross-validation
- D. All of the above
Answer: B
Explanation:
Explanation
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.
NEW QUESTION # 48
Which ones are the known limitations of using External function?
- A. Currently, external functions cannot be shared with data consumers via Secure Data Sharing.
- B. External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly
- C. Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.
- D. An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.
Answer: A,B,C,D
NEW QUESTION # 49
Which of the following is a common evaluation metric for binary classification?
- A. F1 score
- B. Area under the ROC curve (AUC)
- C. Mean squared error (MSE)
- D. Accuracy
Answer: B
Explanation:
Explanation
The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.
NEW QUESTION # 50
Which of the following method is used for multiclass classification?
- A. one vs another
- B. one vs rest
- C. all vs one
- D. loocv
Answer: B
Explanation:
Explanation
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data.
The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.
NEW QUESTION # 51
Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?
- A. Streamlit
- B. Streamsets
- C. StreamBI
- D. Rapter
Answer: A
Explanation:
Explanation
Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.
Why Streamlit?
Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:
It is a free and open-source library.
Installing Streamlit is as simple as installing any other python package It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application.
It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.
NEW QUESTION # 52
Which one is not the types of Feature Engineering Transformation?
- A. Normalization
- B. Aggregation
- C. Encoding
- D. Scaling
Answer: B
Explanation:
Explanation
What is Feature Engineering?
Feature engineering is the process of transforming raw data into features that are suitable for ma-chine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available data to build more accurate and efficient machine learning models.
The success of machine learning models heavily depends on the quality of the features used to train them.
Feature engineering involves a set of techniques that enable us to create new features by combining or transforming the existing ones. These techniques help to highlight the most important pat-terns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively.
What is a Feature?
In the context of machine learning, a feature (also known as a variable or attribute) is an individual measurable property or characteristic of a data point that is used as input for a machine learning al-gorithm. Features can be numerical, categorical, or text-based, and they represent different aspects of the data that are relevant to the problem at hand.
For example, in a dataset of housing prices, features could include the number of bedrooms, the square footage, the location, and the age of the property. In a dataset of customer demographics, features could include age, gender, income level, and occupation.
The choice and quality of features are critical in machine learning, as they can greatly impact the ac-curacy and performance of the model.
Why do we Engineer Features?
We engineer features to improve the performance of machine learning models by providing them with relevant and informative input data. Raw data may contain noise, irrelevant information, or missing values, which can lead to inaccurate or biased model predictions. By engineering features, we can extract meaningful information from the raw data, create new variables that capture important patterns and relationships, and transform the data into a more suitable format for machine learning algorithms.
Feature engineering can also help in addressing issues such as overfitting, underfitting, and high di-mensionality. For example, by reducing the number of features, we can prevent the model from be-coming too complex or overfitting to the training data. By selecting the most relevant features, we can improve the model's accuracy and interpretability.
In addition, feature engineering is a crucial step in preparing data for analysis and decision-making in various fields, such as finance, healthcare, marketing, and social sciences. It can help uncover hidden insights, identify trends and patterns, and support data-driven decision-making.
We engineer features for various reasons, and some of the main reasons include:
Improve User Experience: The primary reason we engineer features is to enhance the user experience of a product or service. By adding new features, we can make the product more intuitive, efficient, and user-friendly, which can increase user satisfaction and engagement.
Competitive Advantage: Another reason we engineer features is to gain a competitive advantage in the marketplace. By offering unique and innovative features, we can differentiate our product from competitors and attract more customers.
Meet Customer Needs: We engineer features to meet the evolving needs of customers. By analyzing user feedback, market trends, and customer behavior, we can identify areas where new features could enhance the product's value and meet customer needs.
Increase Revenue: Features can also be engineered to generate more revenue. For example, a new feature that streamlines the checkout process can increase sales, or a feature that provides additional functionality could lead to more upsells or cross-sells.
Future-Proofing: Engineering features can also be done to future-proof a product or service. By an-ticipating future trends and potential customer needs, we can develop features that ensure the product remains relevant and useful in the long term.
Processes Involved in Feature Engineering
Feature engineering in Machine learning consists of mainly 5 processes: Feature Creation, Feature Transformation, Feature Extraction, Feature Selection, and Feature Scaling. It is an iterative process that requires experimentation and testing to find the best combination of features for a given problem. The success of a machine learning model largely depends on the quality of the features used in the model.
Feature Transformation
Feature Transformation is the process of transforming the featuresinto a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.
Types of Feature Transformation:
Normalization: Rescaling the features to have a similar range, such as between 0 and 1, to prevent some features from dominating others.
Scaling: Rescaling the features to have a similar scale, such as having a standard deviation of 1, to make sure the model considers all features equally.
Encoding: Transforming categorical features into a numerical representation. Examples are one-hot encoding and label encoding.
Transformation: Transforming the features using mathematical operations to change the distribution or scale of the features. Examples are logarithmic, square root, and reciprocal transformations.
NEW QUESTION # 53
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