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DEA-C02 Guaranteed Success - Valid DEA-C02 Test Cram

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DEA-C02 Guaranteed Success - Valid DEA-C02 Test Cram

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q240-Q245):NEW QUESTION # 240
You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?
  • A. Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
  • B. Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
  • C. Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
  • D. Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
  • E. Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
Answer: A,B,C
Explanation:
Snowflake encrypts data at rest by default (option A). Using network policies to restrict access based on IP address (option D) provides a layer of network security. Encrypting data client-side (option E) adds another layer of security beyond Snowflake's default encryption. Masking policies (option B) are applied to tables, not directly to the stage. While temporary stages exist, they are related to session lifecycle, not security inherently (option C).

NEW QUESTION # 241
Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?
  • A. Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
  • B. Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
  • C. Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
  • D. Create a temporary table with average salaries per department, then join it back to the original DataFrame.
  • E. Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
Answer: B
Explanation:
Using the 'window' function (Option C) is the most efficient. Window functions are specifically designed for this type of calculation, allowing you to perform aggregations over a subset of rows related to the current row (in this case, employees in the same department) without the overhead of joins or subqueries. Option A, B and E are less efficient due to join and subquery overhead. UDFs are also typically slower than built-in functions.

NEW QUESTION # 242
Your team is developing a set of complex analytical queries in Snowflake that involve multiple joins, window functions, and aggregations on a large table called 'TRANSACTIONS. These queries are used to generate daily reports. The query execution times are unacceptably high, and you need to optimize them using caching techniques. You have identified that the intermediate results of certain subqueries are repeatedly used across different reports, but they are not explicitly cached. Given the following options, which combination of strategies would MOST effectively utilize Snowflake's caching capabilities to optimize these analytical queries and improve report generation time?
  • A. Use temporary tables to store the intermediate results of the subqueries. These tables will be automatically cached by Snowflake and can be reused by subsequent queries within the same session.
  • B. Create common table expressions (CTEs) for the subqueries and reference them in the main query. CTEs will force Snowflake to cache the results of the subqueries, improving performance.
  • C. Utilize the "RESULT_SCAN' function in conjunction with the query ID of the initial subquery execution to explicitly cache and reuse the results in subsequent queries. This approach requires careful management of query IDs.
  • D. Create materialized views that pre-compute the intermediate results of the subqueries. This will allow Snowflake to automatically refresh the materialized views when the underlying data changes and serve the results directly from the cache.
  • E. Consider using 'CACHE RESULT for particularly expensive subqueries or views. This is a hint to snowflake to prioritize caching the result set for future calls.
Answer: D,E
Explanation:
Creating materialized views (D) for the intermediate results is the most effective approach, as Snowflake automatically manages the refresh and caching. 'CACHE RESULT (E) Provides a way to explicitly cache the results. Temporary tables (A) are session-specific and not suitable for persistent caching across reports. CTEs (B) do not guarantee caching and are primarily for query readability. 'RESULT SCAN' (C) is complex to manage and requires manual tracking of query IDs. Therefore, a combination of materialized views and CACHE RESULT will provide the best caching strategy.

NEW QUESTION # 243
A data engineer is tasked with creating a Snowpark Python UDF to perform sentiment analysis on customer reviews. The UDF, named 'analyze_sentiment' , takes a string as input and returns a string indicating the sentiment ('Positive', 'Negative', or 'Neutral'). The engineer wants to leverage a pre-trained machine learning model stored in a Snowflake stage called 'models'. Which of the following code snippets correctly registers and uses this UDF?

  • A. Option C
  • B. Option B
  • C. Option A
  • D. Option E
  • E. Option D
Answer: E
Explanation:
The most concise and recommended way to define a Snowpark UDF in Python is using the @F.udf decorator. This decorator automatically handles registration with Snowflake and simplifies the code. It also correctly specifies the 'return_type' , 'input_types' , and required packages'. Options A, B, C and E are either missing the decorator or have issues with specifying input types or session usage. The session.add_packageS is not a proper way to define packages used by UDFs and 'StringType' is not imported from 'snowflake.snowpark.types , so the correct way is to set return_type and input_types within the decorator.

NEW QUESTION # 244
You're tasked with building a data pipeline using Snowpark Python to incrementally load data into a target table 'SALES SUMMARY from a source table 'RAW SALES. The pipeline needs to ensure that only new or updated records from 'RAW SALES are merged into 'SALES SUMMARY' based on a 'TRANSACTION ID'. You want to use Snowpark's 'MERGE' operation for this, but you also need to handle potential conflicts and log any rejected records to an error table 'SALES SUMMARY ERRORS'. Which of the following approaches offers the MOST robust and efficient solution for handling errors and ensuring data integrity within the MERGE statement?
  • A. Use the 'WHEN MATCHED THEN UPDATE' clause to update existing records and the 'WHEN NOT MATCHED THEN INSERT clause to insert new records. Implement a separate process to periodically compare 'SALES_SUMMARY with 'RAW_SALES' to identify and log any inconsistencies.
  • B. Use a single 'MERGE statement with 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses. Capture rejected records by leveraging the ' SYSTEM$PIPE STATUS function after the 'MERGE operation to identify rows that failed during the merge.
  • C. Employ the 'MERGE statement with 'WHEN MATCHED THEN UPDATE' and 'WHEN NOT MATCHED THEN INSERT clauses, and use a stored procedure that executes the 'MERGE statement and then conditionally inserts rejected records into the 'SALES SUMMARY ERRORS' table based on criteria defined within the stored procedure. This will use the table function on the output.
  • D. Utilize the 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses with a 'WHERE' condition in each clause to filter out potentially problematic records. Log these filtered records to using a separate 'INSERT statement after the 'MERGE operation.
  • E. Incorporate an 'ELSE clause in the 'MERGE' statement to capture records that do not satisfy the update or insert conditions due to data quality issues. Use this 'ELSE clause to insert rejected records into 'SALES SUMMARY ERRORS'
Answer: C
Explanation:
Option E provides the most robust solution. Using a stored procedure to execute the MERGE allows for more complex error handling logic. Critically, the result_scan function of the MERGE query can then be used to identify and analyze the success or failure of each individual record processed within the MERGE. This avoids separate processes or post-merge comparisons and is therefore more robust. Option A requires a separate process for inconsistency checking, which is less efficient and may miss real-time errors. Options B, C, and D do not offer a reliable and atomic way to capture and log all rejected records. The SYSTEM$PIPE_STATUS function is relevant for Snowpipe, not direct MERGE operations.

NEW QUESTION # 245
......
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