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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q349-Q354):

NEW QUESTION # 349
You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A. Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
B. Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
C. Use a stored procedure to parse the JSON data and insert values into the table row by row.
D. Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
E. Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.

Answer: A

Explanation:
Using a 'COPY command with a 'TRANSFORM' clause and JavaScript UDFs allows for efficient parsing and extraction of values during the load process. This minimizes the amount of data stored in the 'VARIANT column and avoids expensive query-time parsing. Stored procedures perform row by row operations which are inefficient. Using Flatten functions could be useful to denormalise json, but javascript parsing during load is better. Snowpipe and auto-ingest just move the challenge to a real-time streaming scenario, which may not be optimized for transforming data into a relational structure.

NEW QUESTION # 350
A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?

A. Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
B. Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.
C. Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
D. Drop the current clustered table and create a new table with partition by clauses
E. Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.

Answer: B,E

Explanation:
A low 'effective_clustering_ratio' and high 'average_overlaps' indicate that the data is not well-clustered, leading to inefficient query performance. Reclustering the table (A) reorganizes the data based on the clustering key, improving clustering. Creating a schedule (D) ensures that the table remains well-clustered over time. Dropping the clustering key and recreating it with a hash on (B) is unlikely to improve performance for date-range queries. Creating new table via 'CREATE TABLE AS SELECT statement is not the right way. In addition, 'Partition By' clause doesn't exist in Snowflake.

NEW QUESTION # 351
You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?

A. Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
B. Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
C. Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
D. Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
E. Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.

Answer: D

Explanation:
The ' EXPLAIN' command provides detailed information about the query execution plan. By examining the 'partitionsTotal' and 'partitionsScanned' values, you can directly see how many micro-partitions Snowflake considered vs. how many it actually scanned. A large difference suggests that the clustering is effectively pruning partitions based on the 'EVENT_TIMESTAMP' filter. While 'SYSTEM$CLUSTERING_INFORMATION' provides a general overview of clustering quality, it doesn't tell you how it's performing for a specific query. Looking at query history or checking that the clustering key is defined is useful for verifying basic setup but doesn't directly diagnose the effectiveness for slow queries.

NEW QUESTION # 352
A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A. Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.
B. Restore the entire Snowflake account to a previous point in time before the table was truncated.
C. Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
D. Contact Snowflake support and request them to restore the table from a system-level backup.
E. Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.

Answer: C

Explanation:
Option D is the most efficient and reliable. Cloning the table using Time Travel to a point before the truncation allows quick recovery with minimal data loss. The clone can then replace the truncated table. Option A relies on Snowflake support, which can be slow. Option B, UNDROP TABLE command, if the data retention period has passed or data has been purged then we cannot use it. Option C is manual and error-prone. Option E is an extreme measure and impacts the entire account.

NEW QUESTION # 353
You are tasked with building a data pipeline using Snowpark to process sensor data from IoT devices. The data arrives in near real-time as JSON payloads, and you need to transform and load it into a Snowflake table named 'SENSOR DATA'. The transformation logic involves extracting specific fields, converting data types, and filtering out records based on a timestamp. Consider performance optimization for large data volumes. Which of the following approaches, in combination, would be MOST efficient for this scenario?

A. Creating an external table pointing to the JSON data in cloud storage and using Snowpark DataFrames to read the external table, apply transformations, and load the result into 'SENSOR DATA'.
B. Using a Snowpark Python UDF to parse JSON and perform transformations, loading the result into a temporary table, and then merging into 'SENSOR DATA'.
C. Using a stored procedure written in Java to parse the JSON data and insert directly into the "SENSOR DATA' table.
D. Leveraging Snowflake's native JSON parsing functions within a SQL transformation step implemented as a Snowpark DataFrame operation, combined with a Snowpipe for initial data ingestion into a staging table.
E. Employing Snowpipe to ingest the raw JSON data into a VARIANT column in a staging table, followed by a Snowpark DataFrame operation using 'functions.get' to extract and transform the data, and finally loading into 'SENSOR DATA'

Answer: D,E

Explanation:
Options B and E, used in combination, offer the best performance. Snowpipe provides efficient near real-time ingestion into a VARIANT column. Then, using Snowpark DataFrames with Snowflake's native JSON parsing functions like 'functions.get' and 'functions.to_timestamp' allows for vectorized operations within Snowflake's engine, minimizing data movement and maximizing processing speed. This combination avoids the overhead of UDFs (Option A) or external tables (Option C), and leverages the strengths of both Snowpipe and Snowpark. A Java stored procedure (Option D) would likely be less performant than leveraging Snowpark's DataFrame API.

NEW QUESTION # 354
......

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