Snowflake Snowflake Certifications DSA-C02 Questions & Answers

• Question 51:

  The most widely used metrics and tools to assess a classification model are:

  A. Confusion matrix

  B. Cost-sensitive accuracy

  C. Area under the ROC curve

  D. All of the above

  Correct Answer: D

• Question 52:

  Which ones are the correct rules while using a data science model created via External function in Snowflake? Choose all apply.

  A. External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.

  B. External functions can be overloaded.

  C. An external function can appear in any clause of a SQL statement in which other types of UDF can appear.

  D. External functions can accept Model parameters.

  Correct Answer: ABCD

  Explanation:

  From the perspective of a user running a SQL statement, an external function behaves like any other UDF . External functions follow these rules:

  External functions return a value.

  External functions can accept parameters.

  An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:

  1.select my_external_function_2(column_1, column_2) 2.from table_1;

  1.select col1

  2.from table_1

  3.where my_external_function_3(col2) < 0;

  1.create view view1 (col1) as

  2.select my_external_function_5(col1)

  3.from table9;

  An external function can be part of a more complex expression:

  1.select upper(zipcode_to_city_external_function(zipcode)) 2.from address_table;

  The returned value can be a compound value, such as a VARIANT that contains JSON. External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input

  parameters).

• Question 53:

  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. Partial dependence plots(PDP)

  D. FULL dependence plots (FDP)

  Correct Answer: C

  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.

• Question 54:

  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

  Correct Answer: B

  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.

• Question 55:

  Mark the incorrect statement regarding usage of Snowflake Stream and Tasks?

  A. Snowflake automatically resizes and scales the compute resources for serverless tasks.

  B. Snowflake ensures only one instance of a task with a schedule (i.e. a standalone task or the root task in a DAG) is executed at a given time. If a task is still running when the next scheduled execution time occurs, then that scheduled time is skipped.

  C. Streams support repeatable read isolation.

  D. An standard-only stream tracks row inserts only.

  Correct Answer: D

  Explanation:

  All are correct except a standard-only stream tracks row inserts only. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).

• Question 56:

  Which of the Following is not type of Windows function in Snowflake? Choose 2.

  A. Rank-related functions.

  B. Window frame functions.

  C. Aggregation window functions.

  D. Association functions.

  Correct Answer: CD

  Explanation:

  Window Functions

  A window function operates on a group ("window") of related rows. Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one

  output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function. Some window functions are order-sensitive. There are two main types of

  order-sensitive window functions:

  Rank-related functions.

  Window frame functions.

  Rank-related functions list information based on the "rank" of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

  Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.

• Question 57:

  Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?

  A. Reinforcement learning

  B. Unsupervised learning

  C. Artificial learning

  D. Supervised learning

  Correct Answer: A

  Explanation:

  Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few

  random experiments. Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect

  the probability that the other event will occur.

• Question 58:

  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

  Correct Answer: C

  Explanation:

  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 59:

  Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. Which of the following Data transformation SQL function he can used to achieve the same?

  A. He could not apply Transformation on Stream table data.

  B. lateral flatten()

  C. METADATA$ACTION ()

  D. Transpose()

  Correct Answer: B

  Explanation: To know about lateral flatten SQL Function, please refer: https://docs.snowflake.com/en/sql-reference/constructs/join-lateral#example-of-using- lateral-with-flatten

• Question 60:

  Skewness of Normal distribution is ___________

  A. Negative

  B. Positive

  C. 0

  D. Undefined

  Correct Answer: C

  Explanation:

  Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for mathematical proofs, you can refer to books or websites that speak on the same in detail.