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

1. You are tasked with calculating the daily moving average of sales for each product category in your Snowflake data warehouse using Snowpark Python. You need to handle cases where there might be missing sales data for certain days. You have the following sales data available in a table named with columns: (DATE), (VARCHAR), and 'sales_amount' (NUMBER). Which of the following Snowpark Python code snippets correctly calculates the 7-day moving average of sales amount per product category, filling in missing sales days with 0, and handles potential division by zero?

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

2. You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

A) Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
B) Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
C) Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
D) Share the underlying table and provide the consumer account with the anonymization algorithm separately.
E) Create a view that calls the secure UDF and share that view with the consumer account.

3. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Use the 'UNDROP DATABASE PRODUCTION command.
B) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
C) Contact Snowflake Support and request restoration from Fail-safe.
D) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
E) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'

4. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
B) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
C) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
D) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
E) Create separate tables for each "PRODUCT_ID' to improve query performance.

5. 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 B
B) Option C
C) Option A
D) Option D
E) Option E

Solutions: