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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q154-Q159):

NEW QUESTION # 154
You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A. Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
B. Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.
C. Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
D. Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
E. Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.

Answer: B

Explanation:
Option C is the most efficient and self-contained approach using native Snowflake features. Embedding the retry logic within the stored procedure called by 'task_ff allows for fine-grained control over the retry process, exception handling, and delay implementation. The retry count limit prevents infinite loops. Option A, while technically feasible, involves querying the TASK HISTORY view, which can be less efficient. Option B requires creating and managing an additional task. Option D introduces external dependencies, making the solution more complex. Option E does not address the retry mechanism.

NEW QUESTION # 155
You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A. Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.
B. Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
C. Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
D. Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
E. Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.

Answer: C

Explanation:
Option C provides the best balance. A join ensures that all rows from 'dfl are preserved, even if there's no matching row in 'df2. By replacing 'df2 with 'df_default' when 'df2' is unavailable/empty, the pipeline continues processing 'dfl with predefined defaults, maintaining data integrity. Option A only bypasses the exception without using default value. Option B is incorrect because 'fillna' may not handle the case where 'df2 is unavailable and the whole join fails. Option D may only help with performance but does not guarantee that df2 will be available at all times. Option E is also not efficient due to UDF being slower than joins.

NEW QUESTION # 156
Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

A. The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.
B. The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
C. The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
D. The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
E. While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.

Answer: A

Explanation:
JavaScript UDFs in Snowflake are sandboxed, preventing SQL injection vulnerabilities. The SECURE keyword hides the UDF definition from users without appropriate privileges, but not from users with sufficient privileges like ACCOUNTADMIN. The UDF explicitly returns NULL for invalid unit values.

NEW QUESTION # 157
You're managing a Snowflake data warehouse and need to create a development environment for testing a complex stored procedure that updates a critical table, 'SALES DATA'. The procedure is located in the 'PRODUCTION' database and you want to ensure minimal impact to the production environment during development. You decide to use cloning and time travel. Which of the following strategies is the MOST efficient and safest approach to achieve this, minimizing downtime and resource consumption in production?

A. Create a snapshot of the 'SALES DATA' table using Time Travel at a specific timestamp (e.g., 1 hour ago), then clone only the stored procedure, updating it to point to the Time Travel version of 'SALES DATA' in the development environment. This provides a consistent dataset for testing while minimizing the impact on production and cloned data volumes.
B. Clone the schema in which 'SALES_DATX is stored along with the stored procedure. Use time travel on the cloned schema to revert all objects in the schema to a point in time before the stored procedure was last run, then update the stored procedure to point to the cloned schema. This gives a consistent starting point for testing in development.
C. Clone the entire 'PRODUCTION' database into a new development database. This ensures developers have access to all necessary data and dependencies but consumes significant storage and may impact production performance during the cloning process.
D. Clone only the 'SALES DATA' table into a development database. This minimizes storage consumption but requires developers to manually recreate or mock any dependencies the stored procedure has on other tables in the 'PRODUCTION' database.
E. Clone the "PRODUCTION' database. Immediately after cloning, use Time Travel to revert the 'SALES_DATR table in the development database to a state before the stored procedure was last run in production. Then clone the stored procedure itself. This gives a starting point without the procedure's impact.

Answer: B

Explanation:
Option E offers the best balance of minimal impact and realistic testing. Cloning the entire database (A) is resource-intensive. Cloning only the table (B) requires significant manual setup to address dependencies. Option C might result in unpredictable behavior if any data dependencies exist that rely on related tables. Option D is almost correct, but the risk is that other objects in the 'PRODUCTION' database schema might change resulting in incomplete testing. Cloning the schema and using Time Travel on the schema level before updating the procedure gives the most consistent and efficient development setup and the best balance.

NEW QUESTION # 158
You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

A. Enable search optimization directly on the 'EVENT DATA' column.
B. Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
C. Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.
D. Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
E. Increase the warehouse size.

Answer: B

Explanation:
Extracting the frequently queried key-value pairs into separate virtual columns and then enabling search optimization on these columns is the most effective approach (Option B). Snowflake's search optimization works best on columns with well-defined data types. Direct search optimization on a JSON column (Option A) is not directly supported and will not provide the desired performance benefits. Materialized views (Option C) are an option, but virtual columns are generally more lightweight for this scenario. Converting to VARCHAR (Option D) is not the correct approach for JSON data and would prevent proper JSON parsing and filtering. Increasing the warehouse size (Option E) might improve overall performance but doesn't specifically address the JSON filtering bottleneck.

NEW QUESTION # 159
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