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

1. You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
B) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
C) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
D) Set the 'value.converter.schemas.enable' property to 'true'.
E) Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.

2. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

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

3. A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?

A) Implement a custom application using the Snowflake JDBC driver to periodically scan table schemas, detect PII columns, and apply tags using dynamic SQL.
B) Manually tag each column containing PII using the Snowflake web UI or the 'ALTER TABLE ... ALTER COLUMN ... SET TAG' command. Train data stewards to identify and tag new columns.
C) Create a Snowflake Task that runs daily, querying the INFORMATION SCHEMCOLUMNS view, identifying potential PII columns based on regular expressions, and then executing ALTER TABLE ... ALTER COLUMN ... SET TAG commands.
D) Implement a stored procedure that leverages external functions to call a Python script hosted on AWS Lambda, which uses a machine learning model to identify PII and apply Snowflake tags.
E) Use Snowflake's Event Tables in conjunction with a stream and task. Configure the stream to capture DDL changes, and the task to evaluate new columns and apply the tag based on the column metadata using regular expressions.

4. You're working on a data transformation pipeline in Snowflake. You need to create a SQL UDF called that accepts the following parameters: 'price' (NUMBER) (NUMBER) (BOOLEAN) The function should calculate the final price after applying the discount. If the customer , an additional 5% discount should be applied on top of the initial discount. Choose all the valid SQL UDF definitions that accurately implement the requirements. Only one Discount Percentage needs to apply to the base price.

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

5. You have a Snowflake table 'ORDERS with columns 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, and 'TOTAL AMOUNT. You notice that many queries filtering by 'ORDER DATE are slow, even after enabling query acceleration. You decide to implement a caching strategy to improve performance. Which of the following approaches will be most effective in leveraging Snowflake's caching capabilities and improving the performance of date-filtered queries, especially when the data volume for each date is large and varied? Assume virtual warehouse is medium size.

A) Create a clustered table on 'ORDER_DATE. This will physically organize the data on disk, allowing Snowflake to quickly retrieve the relevant data for date- filtered queries.
B) Apply a WHERE clause with a date range in all the SELECT statements. This forces the metadata caching.
C) Increase the data retention period for the 'ORDERS' table. A longer retention period will ensure that more data is available in the Snowflake cache.
D) Use after running a query filtered by 'ORDER_DATE'. This will cache the result of the query in the current session for subsequent queries with the same filter.
E) Create a materialized view that pre-aggregates the data by 'ORDER_DATE , such as calculating the sum of 'TOTAL_AMOUNT for each date. This will allow Snowflake to serve the results directly from the materialized view for queries that require aggregation.

Solutions: