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

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

2. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
B) Set 'snowflake.ingest.file.name' to an existing file in a stage.
C) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
D) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
E) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.

3. You are tasked with building a data pipeline that ingests data from various sources into Snowflake, processes it, and then writes the final results back to a data lake in AWS S3, partitioned by date. The data in S3 should be queryable by other applications outside of Snowflake. You choose to use Snowflake Iceberg tables for this purpose. Which of the following is the correct SQL statement to create an Iceberg table 'analytics.public.daily_summary' in Snowflake, backed by an S3 bucket 's3://your-bucket/data/daily_summary/', partitioned by the column, and specifying 'parquet' as the file format?

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

4. You have a base table 'ORDERS' with columns 'ORDER ID, 'CUSTOMER D', 'ORDER DATE, and 'ORDER AMOUNT'. You need to create a view that aggregates the total order amount per customer per month. However, for data governance purposes, you need to ensure that the view only shows data for the last 3 months. What is the MOST efficient and secure way to create this view in Snowflake?

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

5. You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A) Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
B) Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
C) Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
D) Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
E) Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

Solutions: