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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:

Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?

A) Use the 'functools.lru_cache' decorator to cache the results of the 'get_customer details' function within the UDF's scope. This will automatically cache the most recently used API responses.
B) Persist the API responses in a temporary table within Snowflake. The UDF will first query the temporary table for the customer ID; if found, return the cached data. Otherwise, call the API and store the response in the temporary table for future use.
C) Utilize Snowflake's built-in caching mechanisms (result caching) by ensuring the UDF is deterministic and only depends on its input parameters. Snowflake will automatically cache the results of the UDF for subsequent calls with the same input.
D) Create a global dictionary within the UDF to store the API responses, using the customer ID as the key. Before calling the API, check if the customer ID exists in the dictionary; if it does, return the cached response. This approach will keep cached values during the session.
E) Leverage external caching services like Redis by making API calls to Redis from the UDF to store and retrieve cached API responses. This would require configuring Snowflake to connect with external systems.

2. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
B) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
C) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
D) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
E) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.

3. 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) Store the encryption key directly within the JavaScript UDF code as a string variable.
B) Pass the encryption key as an argument to the UDF each time it is called.
C) 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.
D) Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
E) Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.

4. You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

A) Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
B) Create a secure view that joins the tables and share only the secure view using a data share.
C) Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
D) Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
E) Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.

5. You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform real-time aggregations, and store the results in a Snowflake table. The data stream is highly variable, with occasional spikes in traffic that overwhelm your current Snowpark setup, leading to significant latency in processing. Which of the following strategies, either individually or in combination, would be MOST effective to handle these traffic spikes and ensure near real-time processing?

A) Implement a message queuing system (e.g., RabbitMQ, Kafka) between Kafka and your Snowpark application to buffer incoming data during traffic spikes.
B) Configure the Snowflake warehouse used by your Snowpark application to use auto-suspend and auto-resume with a short auto-suspend time to minimize costs during periods of low traffic.
C) Use 'CACHE RESULT for all queries in snowpark that use Kafka
D) Use Snowpark's async actions (e.g., to offload data processing to separate threads or processes, allowing your main Snowpark application to continue receiving data.
E) Implement dynamic warehouse scaling. Utilize Snowflake's Resource Monitors and the ability to programmatically resize warehouses through Snowpark. Monitor the queue depth or latency of your Snowpark application, and dynamically scale up the warehouse size when thresholds are exceeded. Then, scale it back down when traffic subsides.

Solutions: