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

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

1. You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A) Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE
B) Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
C) Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
D) Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
E) Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.

2. You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

A) Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
B) Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
C) Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
D) Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
E) Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.

3. A Snowflake data warehouse is experiencing performance degradation. A critical reporting dashboard, which relies on a complex SQL query, is taking significantly longer to execute. You've examined the query profile and identified that a user-defined function (UDF) called 'PARSE ADDRESS, written in Python, is consuming a large portion of the execution time. The 'PARSE ADDRESS UDF is used to parse address strings into individual components (street, city, state, zip). What are the most effective strategies to improve the performance of this query, assuming it's difficult to rewrite the query without the UDF?

A) Utilize external functions with a more performant compute service (e.g. AWS Lambda) if the environment allows.
B) Create a materialized view that pre-calculates the output of the 'PARSE ADDRESS UDF for frequently queried address values and joins with the original table.
C) convert the Python UDF 'PARSE_ADDRESS' into a Java UDE
D) Increase the virtual warehouse size to compensate for the UDF's overhead.
E) Rewrite the Python UDF 'PARSE ADDRESS in SQL using Snowflake's built-in string functions.

4. 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) 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.
B) 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.
C) 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.
D) 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.
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.

5. You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A) Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
B) Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
C) Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.
D) Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.
E) Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.

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