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

1. A data engineer notices that a daily ETL job loading data into a Snowflake table 'TRANSACTIONS' is consistently taking longer than expected. The table is append-only and partitioned by 'TRANSACTION DATE. The engineer observes high 'Remote Spill' during the load process and suspect that micro-partition pruning isn't working effectively. Which of the following approaches would BEST address the performance issue, assuming you have already considered increasing warehouse size?

A) Examine the data load process to ensure the data is loaded in 'TRANSACTION_DATE order. If not, sort the data by 'TRANSACTION_DATE before loading.
B) Re-create the 'TRANSACTIONS' table with a larger virtual warehouse and re-load the entire dataset.
C) Partition the data in the source system by 'TRANSACTION DATE' and load data in parallel corresponding to each partition.
D) Implement data skipping by creating a masking policy on the 'TRANSACTION_DATE column.
E) Enable automatic clustering on the 'TRANSACTION_DATE column of the 'TRANSACTIONS table.

2. A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

A) Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
B) Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
C) Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'
D) Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
E) It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.

3. You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

4. You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

A) Avoid large or small number and stick to the limited range of input values.
B) Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
C) Cast input arguments and the result to 'FLOAT within the UDF.
D) Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
E) Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.

5. You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?