Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 41:

  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 1
  B. no change
  C. by intercept
  D. by its slope
  D. by its slope

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  Explanation/Reference:

  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.

• Question 42:

  Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-ing. Which of the following options? Choose 2.

  A. Grant privileges on objects to a share via Account role.
  B. Grant privileges on objects directly to a share.
  C. Grant privileges on objects to a share via a database role.
  D. Grant privileges on objects to a share via a third-party role.
  B. Grant privileges on objects directly to a share.
  C. Grant privileges on objects to a share via a database role.

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  Explanation/Reference:

  What is a Share?

  Shares are named Snowflake objects that encapsulate all of the information required to share a database.

  Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using either or both of the following options:

  Option 1: Grant privileges on objects to a share via a database role. Option 2: Grant privileges on objects directly to a share. You choose which accounts can consume data from the share by adding the accounts to the share.

  After a database is created (in a consumer account) from a share, all the shared objects are accessible to users in the consumer account. Shares are secure, configurable, and controlled completely by the provider account:

  New objects added to a share become immediately available to all consumers, providing real-time access to shared data.

  Access to a share (or any of the objects in a share) can be revoked at any time.

• Question 43:

  Which one is not the feature engineering techniques used in ML data science world?

  A. Imputation
  B. Binning
  C. One hot encoding
  D. Statistical
  D. Statistical

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  Explanation/Reference:

  Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.

  What is a feature?

  Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often

  known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature

  is an attribute that impacts a problem or is useful for the problem.

  What is Feature Engineering?

  Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model

  for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model. Some of the popular feature engineering

  techniques include:

  1.

  Imputation Feature engineering deals with inappropriate data, missing values,human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for handling irregularities within the dataset. For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:

  For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns. For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.

  2.

  Handling Outliers

  Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This

  technique first identifies the outliers and then remove them out.

  Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than acertain value, it can be considered as an outlier. Z-score can also be

  used to detect outliers.

  3.

  Log transform

  Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after

  transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.

  4.

  Binning

  In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisydata. However, one of the popular techniques of feature engineering,

  "binning", can be used to normalize the noisy data. This process involves segmenting different features into bins.

  5.

  Feature Split

  As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset. The

  feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.

  6.

  One hot encoding

  One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good

  prediction. It enables group theof categorical data without losing any information.

• Question 44:

  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())

  A. aggregate({'A':len, 'B':np.sum})
  B. Computes Sum of column A values
  C. Computes length of column A
  D. Computes length of column A and Sum of Column B values of each group
  E. Computes length of column A and Sum of Column B values
  C. Computes length of column A

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  Explanation/Reference:

  Computes length of column A and Sum of Column B values of each group

• Question 45:

  Which of the following metrics are used to evaluate classification models?

  A. Area under the ROC curve
  B. F1 score
  C. Confusion matrix
  D. All of the above
  D. All of the above

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  Explanation/Reference:

  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.

• Question 46:

  Which ones are the type of visualization used for Data exploration in Data Science? Choose 3.

  A. Heat Maps
  B. Newton AI
  C. Feature Distribution by Class
  D. 2D-Density Plots
  E. Sand Visualization
  A. Heat Maps
  D. 2D-Density Plots
  E. Sand Visualization

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  Explanation/Reference:

  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

• Question 47:

  Which is the visual depiction of data through the use of graphs, plots, and informational graphics?

  A. Data Interpretation
  B. Data Virtualization
  C. Data visualization
  D. Data Mining
  D. Data Mining

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  Explanation/Reference:

  Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics. Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.

• Question 48:

  Which one is the incorrect option to share data in Snowflake?

  A. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
  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 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.

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  Explanation/Reference:

  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.

• Question 49:

  Which tools helps data scientist to manage ML lifecycle and Model versioning? Choose 2.

  A. MLFlow
  B. Pachyderm
  C. Albert
  D. CRUX
  A. MLFlow
  B. Pachyderm

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  Explanation/Reference:

  Model versioning in a way involves tracking the changes made toan ML model that has been previously built. Put differently, it is the process of making changes to the configurations of an ML Model. From another perspective, we can see model versioning as a feature that helps Machine Learning Engineers, Data Scientists, and related personnel create and keep multiple versions of the same model. Think of it as a way of taking notes of the changes you make to the model through tweaking hyperparameters, retraining the model with more data, and so on. In model versioning, a number of things need to be versioned, to help us keep track of important changes. I'll list and explain them below: Implementation code: From the early days of model building to optimization stages, code or in this case source code of the model plays an important role. This code experiences significant changes during optimization stages which can easily be lost if not tracked properly. Because of this, code is one of the things that are taken into consideration during the model versioning process. Data: In some cases, training data does improve significantly from its initial state during model op-timization phases. This can be as a result of engineering new features from existing ones to train our model on. Also there is metadata (data about your training data and model) to consider versioning. Metadata can change different times over without the training data actually changing. We need to be able to track these changes through versioning Model: The model is a product of the two previous entities and as stated in their explanations, an ML model changes at different points of the optimization phases through hyperparameter setting, model artifacts and learning coefficients. Versioning helps take record of the different versions of a Machine Learning model. MLFlow and Pachyderm are the tools used to manage ML lifecycle and Model versioning.

• Question 50:

  You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.

  What should you do?

  A. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.
  B. Create a new version of the dataset using only the new data and retrain the model.
  C. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
  D. Retrained your training dataset after correcting data outliers and no need to introduce new data.
  A. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.

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  Explanation/Reference:

  To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

  Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

  These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario,

  retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.