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

1. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
B) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
C) Disable auto-tuning for the warehouse to ensure consistent performance
D) Increase the size of the Snowflake warehouse used for the Snowpark session.
E) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.

2. You are using Snowpipe to ingest data from Azure Blob Storage into a Snowflake table. You have successfully set up the pipe and configured the event notifications. However, you notice that duplicate records are appearing in your target table. After reviewing the logs, you determine that the same file is being processed multiple times by Snowpipe. Which of the following strategies can you implement to prevent duplicate data ingestion, assuming you cannot modify the source data in Azure Blob Storage to include a unique ID or timestamp?

A) Implement idempotent logic within a Snowflake stored procedure that is triggered by a task after the data is loaded by Snowpipe. The stored procedure should identify and remove duplicate rows based on all other columns in the table.
B) Modify the Azure Event Grid subscription configuration to filter events based on file size or creation time to avoid resending events for already processed files.
C) Create a Snowflake stream on the target table and use it to incrementally load data into a separate, deduplicated table using a merge statement with conditional logic to insert or update records based on a combination of columns.
D) Use a data masking policy with the 'MASK' function to obfuscate duplicate records based on their similarity, making them effectively invisible to downstream queries.
E) Configure the Snowpipe definition with the 'PURGE = TRUE parameter. This will ensure that each file is only processed once.

3. You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

A) Option B
B) Option A
C) Option C
D) Option E
E) Option D

4. You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

A) Option B
B) Option A
C) Option C
D) Option E
E) Option D

5. A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A) Recluster the entire table in a single transaction during off-peak hours.
B) Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.
C) Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
D) Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.
E) Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.

Solutions: