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

1. A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A) Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.
B) Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
C) Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.
D) Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
E) Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.

2. Consider a scenario where you have a Snowflake table named 'CUSTOMER DATA' containing customer IDs (INTEGER) and encrypted credit card numbers (VARCHAR). You need to create a secure JavaScript UDF to decrypt these credit card numbers using a custom encryption key stored securely within Snowflake's internal stage, and then mask all but the last four digits of the decrypted number for data protection. Which of the following actions are necessary to ensure both functionality and security while adhering to Snowflake's best practices for UDF development and security?

A) Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
B) Store the encryption key directly within the JavaScript UDF code as a string variable.
C) Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.
D) Pass the encryption key as an argument to the UDF each time it is called.
E) Store the encryption key in a separate file on an internal stage accessible only by the UDF's service account and load the key from the file within the UDF at runtime.

3. You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

A) Add a search optimization strategy to the table on the 'event_time' column.
B) Increase the warehouse size to improve query performance.
C) The table is already partitioned by 'event_time' , so there is no need for further optimization.
D) Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
E) Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.

4. A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A) Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).
B) Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.
C) Increase the warehouse size to accelerate the re-clustering process.
D) Drop the clustering key altogether to avoid re-clustering costs.
E) Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.

5. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

Solutions: