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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

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

2. You are tasked with creating a Snowpark Python UDF that calculates the exponential moving average (EMA) of a time series dataset stored in a Snowflake table named 'SALES DATA'. The table has columns 'TIMESTAMP' (TIMESTAMP_NTZ) and 'SALES' (NUMBER). The EMA should be calculated for each product, identified by the 'PRODUCT ID' column. You want to optimize the calculation by using a Pandas DataFrame within the UDF and leveraging vectorized operations. Which of the following code snippets would be the MOST efficient and correct way to achieve this? Assume 'alpha' is a predefined float variable representing the smoothing factor.

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

3. A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

A) Periodically clone the ' SALES DATA' table to a separate historical table.
B) Implement data clustering on a column that is frequently used in time travel query filters.
C) Decrease the parameter for the 'SALES_DATA' table.
D) Increase the parameter for the 'SALES_DATX table.
E) Create a materialized view that pre-computes the results of common time travel queries.

4. You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

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

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

Solutions: