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Snowflake DEA-C02 Exam Dumps - Reliable Way to Pass Exam Instantly

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q256-Q261):

NEW QUESTION # 256
Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

A. Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.
B. Create a replication group in the source AWS account and add the databases and shared objects to it.
C. Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
D. Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.
E. Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.

Answer: A,B,E

Explanation:
A, C, and D are required steps. Option A: Creating a replication group is essential to define what to replicate. Option C: The REPLICATE privilege allows the target account to pull data from the source. Option D: Network policies are crucial for establishing secure communication. Option B is incorrect; you create a secondary database using 'CREATE DATABASE AS REPLICA OF : after enabling replication on the source, not before. Option E is related to data loading from external stages, not replication itself in this direct account-to-account scenario.

NEW QUESTION # 257
You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

A. Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
B. Using followed by is an efficient approach to calculate unique users per page per day.
C. Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
D. Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
E. Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.

Answer: B,C,E

Explanation:
Option A is correct: Grouping by page URL and the date part of the timestamp, followed by a distinct count of user IDs, accurately calculates unique users per page per day. Option C is correct: Defining the schema ensures data types are correctly mapped and enforced, preventing potential issues during data loading and improving storage efficiency. Option E is correct: Filtering early reduces the data volume for subsequent operations, improving performance.

NEW QUESTION # 258
You have a table named 'EMPLOYEES with a retention period of 1 day. You accidentally deleted several important rows from this table, but you need to recover the data'. You know the deletion occurred 25 hours ago. What actions should be taken to attempt to recover the deleted data, and what outcome can you expect? Assume you are working in an Enterprise edition of Snowflake account.

A. Attempt to use UNDROP TABLE command if the table was dropped. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
B. Since its Enterprise edition of Snowflake account, the Time travel and cloning will work with 7 days retention period, hence attemtp clone table using Time Travel and recover data successfully
C. Attempt to use Time Travel or cloning to recover the data. Expect the recovery to fail because the deletion occurred outside the I-day data retention period.
D. Attempt to use Time Travel to query the table before the deletion and re-insert the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
E. Attempt to clone the table using Time Travel to a point in time before the deletion, then extract the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.

Answer: C

Explanation:
Option D is the correct answer. Snowflake's Time Travel feature allows data recovery within the defined data retention period. Since the deletion occurred 25 hours ago and the retention period is only 1 day (24 hours), the data is no longer recoverable using Time Travel or cloning. It's important to note that even if the deletion was only 25 hours ago, it still outside of the I-day data retention period.

NEW QUESTION # 259
You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

A. Option D
B. Option B
C. Option C
D. Option E
E. Option A

Answer: B

Explanation:
Option B is the most efficient and correct. It uses 'col()' from 'snowflake.snowpark.functions' to properly reference the 'category' and 'price' columns, uses 'isin()' for a more concise and efficient filtering of multiple category values, groups by the category using and calculates the average price with 'avg(col('price')).as_('average_price')'. Option A, C, and D are syntactically incorrect or less efficient ways to accomplish the same task within Snowpark. Option E is incorrect because it utilizes 'to_pandas()' which returns the result as a Pandas DataFrame rather than a Snowpark DataFrame, failing to adhere to the Snowpark environment. While Option D is very similar, it lacks the proper syntax for specifying column references with 'col('category')' in the groupBy and 'col('price')' in the avg function.

NEW QUESTION # 260
You are using Snowpark Python to perform a complex data transformation involving multiple tables and several intermediate dataframes. During the transformation, an error occurs within one of the Snowpark functions, causing the entire process to halt. To ensure data consistency, you need to implement transaction management. Which of the following Snowpark DataFrameWriter options or session configurations would be MOST appropriate for rolling back the entire transformation in case of an error during the write operation to the final target table?

A. Use and manually track intermediate dataframes to delete them in case of failure.
B. Set the session parameter to 'TRUE and wrap the entire transformation within a 'try...except block, explicitly calling in the 'excepts block.
C. Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
D. Set the session parameter to 'TRUE to ensure all DDL operations are atomic and can be rolled back.
E. Use True)' to automatically rollback the write operation if an error occurs during the write process.

Answer: B

Explanation:
Setting 'TRANSACTION_ABORT ON ERROR to 'TRUE ensures that any error will abort the transaction. Wrapping the code in a 'try...except' block allows you to catch the exception and explicitly call 'session.rollback()' to undo any changes made within the transaction. Option A is relevant to DDL operations, not general data transformations. Option B involves manual tracking, which is error-prone. Option D is not a valid Snowpark DataFrameWriter option. Option E, while potentially useful for cancelling queries, does not directly manage transaction rollback from within the Snowpark session.

NEW QUESTION # 261
......

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