Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 31:

  Which type of Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series?

  A. MPP Python UDFs
  B. Scaler Python UDFs
  C. Vectorized Python UDFs
  D. Hybrid Python UDFs
  C. Vectorized Python UDFs

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  Explanation/Reference:

  Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other

  Python UDFs. Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:

  The potential for better performance if your Python code operates efficiently on batches of rows.

  Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.

  When you use vectorized Python UDFs:

  You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code. As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will

  see which batches of input.

• Question 32:

  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
  B. I,II,III
  C. III,IV
  D. II,III,IV
  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
  B. I,II,III

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  Explanation/Reference:

  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.

• Question 33:

  Which type of Machine learning Data Scientist generally used for solving classification and regression problems?

  A. Supervised
  B. Unsupervised
  C. Reinforcement Learning
  D. Instructor Learning
  E. Regression Learning
  A. Supervised

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  Explanation/Reference:

  Supervised Learning

  Overview:

  Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.

  Algorithms:

  Some of the most popularly used supervised learning algorithms are:

  Linear Regression

  Logistic Regression

  Support Vector Machine

  K Nearest Neighbor

  Decision Tree

  Random Forest

  Naive Bayes

  Working:

  Supervised learning algorithms take labelled inputs and map them to the known outputs, which means you already know the target variable. Supervised Learning methods need external supervision to train machine learning models. Hence,

  the name supervised. They need guidance and additional information to return the desired result.

  Applications:

  Supervised learning algorithms are generally used for solving classification and regression problems.

  Few of the top supervised learning applications are weather prediction, sales forecasting, stock price analysis.

• Question 34:

  Mark the Incorrect statements regarding MIN / MAX Functions?

  A. NULL values are skipped unless all the records are NULL
  B. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
  C. The data type of the returned value is the same as the data type of the input values
  D. For compatibility with other systems, the DISTINCT keyword can be specified as an argument for MIN or MAX, but it does not have any effect
  B. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

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  Explanation/Reference:

  NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

• Question 35:

  A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?

  Assuming:

  A database named product_db exists with a schema named product_agg and a table named Item_agg.

  The database, schema, and table will be shared with two accounts named xy12345 and yz23456.

  1.USE ROLE accountadmin;

  2.CREATE DIRECT SHARE product_s;

  3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;

  4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;

  5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s; 6.SHOW GRANTS TO SHARE product_s;

  7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;

  8.SHOW GRANTS OF SHARE product_s;

  A. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
  B. CREATE DIRECT SHARE product_s;
  C. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
  D. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
  C. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;

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  Explanation/Reference:

  CREATE SHARE product_s is the correct Snowsql command to create Share object.

  Rest are correct ones.

  https://docs.snowflake.com/en/user-guide/data-sharing-provider#creating-a-share-using-sql

• Question 36:

  Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?

  A. k-fold cross-validation
  B. Leave-one-out cross-validation
  C. Holdout method
  D. All of the above
  C. Holdout method

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  Explanation/Reference:

  Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.

  Holdout method

  In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test

  dataset.

• Question 37:

  You are training a binary classification model to support admission approval decisions for a college degree program.

  How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?

  A. Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.
  B. Remove the ethnicity feature from the training dataset.
  C. Compare disparity between selection rates and performance metrics across ethnicities.
  D. None of the above.
  C. Compare disparity between selection rates and performance metrics across ethnicities.

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  Explanation/Reference:

  By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

• Question 38:

  Which of the following method is used for multiclass classification?

  A. one vs rest
  B. loocv
  C. all vs one
  D. one vs another
  A. one vs rest

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  Explanation/Reference:

  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.

• Question 39:

  Which of the following is a common evaluation metric for binary classification?

  A. Accuracy
  B. F1 score
  C. Mean squared error (MSE)
  D. Area under the ROC curve (AUC)
  D. Area under the ROC curve (AUC)

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  Explanation/Reference:

  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.

• Question 40:

  What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer? Choose all apply.

  A. Discover and test third-party data sources.
  B. Receive frictionless access to raw data products from vendors.
  C. Combine new datasets with your existing data in Snowflake to derive new business in- sights.
  D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
  A. Discover and test third-party data sources.
  B. Receive frictionless access to raw data products from vendors.
  C. Combine new datasets with your existing data in Snowflake to derive new business in- sights.
  D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.

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  Explanation/Reference:

  As a consumer, you can do the following:

  Discover and test third-party data sources.

  Receive frictionless access to raw data products from vendors.

  Combine new datasets with your existing data in Snowflake to derive new business insights.

  Have datasets available instantly and updated continually for users.

  Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.

  Use the business intelligence (BI) tools of your choice.