Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 11:

  Which Python method can be used to Remove duplicates by Data scientist?

  A. remove_duplicates()
  B. duplicates()
  C. drop_duplicates()
  D. clean_duplicates()
  D. clean_duplicates()

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  Explanation/Reference:

  The drop_duplicates() method removes duplicate rows. dataframe.drop_duplicates(subset, keep, inplace, ignore_index) Remove duplicate rows from the DataFrame:

  1.import pandas as pd

  2.data = {

  3."name": ["Peter", "Mary", "John", "Mary"],

  4."age": [50, 40, 30, 40],

  5."qualified": [True, False, False, False]

  6.}

  7.

  8.df = pd.DataFrame(data)

  9.newdf = df.drop_duplicates()

• Question 12:

  All aggregate functions except _____ ignore null values in their input collection

  A. Count(attribute)
  B. Count(*)
  C. Avg D. Sum
  B. Count(*)

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  Explanation/Reference:

  Count(*)

  * is used to select all values including null.

• Question 13:

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

  A. Publish listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer base.
  B. Publish listings for datasets that can be customized for the consumer.
  C. Share live datasets securely and in real-time without creating copies of the data or im- posing data integration tasks on the consumer.
  D. Eliminate the costs of building and maintaining APIs and data pipelines to deliver data to customers.
  A. Publish listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer base.
  B. Publish listings for datasets that can be customized for the consumer.
  C. Share live datasets securely and in real-time without creating copies of the data or im- posing data integration tasks on the consumer.
  D. Eliminate the costs of building and maintaining APIs and data pipelines to deliver data to customers.

  ---

  Explanation/Reference:

  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.

• Question 14:

  Mark the correct steps for saving the contents of a DataFrame to aSnowflake table as part of Moving Data from Spark to Snowflake?

  A. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the NAME() method. Step 3.Use the dbtable option to specify the table to which data is written. Step 4.Specify the connector options using either the option() or options() method. Step 5.Use the save() method to specify the save mode for the content.
  B. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Specify the connector options using either the option() or options() method. Step 4.Use the dbtable option to specify the table to which data is written. Step 5.Use the save() method to specify the save mode for the content.
  C. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Specify the connector options using either the option() or options() method. Step 4.Use the dbtable option to specify the table to which data is written. Step 5.Use the mode() method to specify the save mode for the content.
  D. Step 1.Use the writer() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Use the dbtable option to specify the table to which data is written. Step 4.Specify the connector options using either the option() or options() method. Step 5.Use the save() method to specify the save mode for the content.
  C. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Specify the connector options using either the option() or options() method. Step 4.Use the dbtable option to specify the table to which data is written. Step 5.Use the mode() method to specify the save mode for the content.

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  Explanation/Reference:

  Moving Data from Spark to Snowflake

  The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:

  1.

  Use the write() method of the DataFrame to construct a DataFrameWriter.

  2.

  Specify SNOWFLAKE_SOURCE_NAME using the format() method.

  3.

  Specify the connector options using either the option() or options() method.

  4.

  Use the dbtable option to specify the table to which data is written.

  5.

  Use the mode() method to specify the save mode for the content.

  Examples

  1.df.write

  2..format(SNOWFLAKE_SOURCE_NAME)

  3..options(sfOptions)

  4..option("dbtable", "t2")

  5..mode(SaveMode.Overwrite)

  6..save()

• Question 15:

  Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3']. What does the ex-pression df[lambda x : x.index.str.endswith('3')] do?

  A. Returns the row name r3
  B. Results in Error
  C. Returns the third column
  D. Filters the row labelled r3
  D. Filters the row labelled r3

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  Explanation/Reference:

  It will Filters the row labelled r3.

• Question 16:

  Skewness of Normal distribution is ___________

  A. Negative
  B. Positive
  D. Undefined
  C

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  Explanation/Reference:

  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.

• Question 17:

  Which of the following cross validation versions may not be suitable 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
  B. Leave-one-out cross-validation

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  Explanation/Reference:

  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.

• Question 18:

  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
  A. Reinforcement learning

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  Explanation/Reference:

  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 19:

  Mark the incorrect statement regarding Python UDF?

  A. Python UDFs can contain both new code and calls to existing packages
  B. For each row passed to a UDF, the UDF returns either a scalar (i.e. single) value or, if defined as a table function, a set of rows.
  C. A UDF also gives you a way to encapsulate functionality so that you can call it repeatedly from multiple places in code
  D. A scalar function (UDF) returns a tabular value for each input row
  D. A scalar function (UDF) returns a tabular value for each input row

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  Explanation/Reference:

  A scalar function (UDF) returns one output row for each input row. The returned row consists of a single column/value

• Question 20:

  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.
  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.

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  Explanation/Reference:

  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).