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DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02)–Trustable New Test Answers

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q284-Q289):

NEW QUESTION # 284
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A,D

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 # 285
You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?

A. Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.
B. The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
C. The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
D. The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
E. The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.

Answer: B

Explanation:
The 'net.snowflake.ingest.errors.FileSizeLimitExceededError' clearly indicates that the size of the data files being ingested is exceeding the maximum allowed size for Snowpipe. While Snowflake does have storage capacity limits, that is not the root cause of this specific error. Splitting the files into smaller sizes prior to uploading will allow Snowpipe to process the data without exceeding the size limit. While network errors can occur, the error message is specific to file size. Snowpipe does not use 'MAX CONCURRENCY parameter. It automatically adjusts the concurrency The file format issues will create different error.

NEW QUESTION # 286
You are tasked with loading data from a set of highly nested JSON files into Snowflake. Some files contain an inconsistent structure where a particular field might be a string in some records and an object in others. You want to avoid data loss and ensure that you capture both string and object representations of the field. What is the most efficient approach to achieve this, minimizing data transformation outside of Snowflake?

A. Pre-process the JSON files using a scripting language (e.g., Python) to transform object representations to string representations before loading them into Snowflake. This ensures consistent data type for the field.
B. Define the field as a VARCHAR in an internal stage and use a COPY INTO statement with the VALIDATE function to identify records with object representations. Load the valid VARCHAR values. Create a separate table for the invalid object representations identified during validation.
C. Create two separate external tables, one with the field defined as VARCHAR and another with the field defined as VARIANT. Load data into both, then UNION the results in a view.
D. Define the field in the external table as VARCHAR. During data loading, use a UDF written in Python or Java to handle the different data types, transforming objects to strings. This approach requires deploying the UDF to Snowflake.
E. Use a single external table with the field defined as VARIANT. During data loading, use the TRY CAST function within a SELECT statement to convert the field to VARCHAR when possible,V otherwise retain the VARIANT representation. Handle further processing in subsequent views or queries.

Answer: E

Explanation:
Option B is the most efficient. Defining the field as VARIANT allows Snowflake to handle different data types within the same column. TRY CAST attempts to convert the field to VARCHAR if it's a string, and retains the VARIANT representation if it's an object, avoiding data loss. This approach minimizes the need for separate tables or external data processing. A, C, D and E involve either creating multiple objects, or external stage which are not efficient.

NEW QUESTION # 287
A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

Answer: A,B

Explanation:
Options A and D are correct. They both correctly define a window specification partitioned by 'product_id' and ordered by 'sale_date' . Both options then calculate the cumulative sum of 'sale_amount over this window. Option A defines the window and applies it to the dataframe whereas Option D applies it to the dataframe in one step. Options B and E are incorrect because the order of partitionBy and orderBy is incorrect when setting the window specification. Option C is incorrect as it does not apply a window function after the orderBy.

NEW QUESTION # 288
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. 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.
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. 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.
D. 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.
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: D

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 # 289
......

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