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

1. You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

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

2. You are designing a data governance strategy for a Snowflake data warehouse. You need to track data lineage for compliance purposes. Specifically, you need to identify all downstream tables that depend on a specific column in a source table. Which combination of Snowflake features and techniques would you use to achieve this goal effectively?

A) Rely solely on user documentation and training to ensure data lineage is properly documented and maintained. Implement strict naming conventions for tables and columns.
B) Use Snowflake's INFORMATION SCHEMA views (TABLES, COLUMNS) and regularly audit user query history to manually reconstruct the data lineage.
C) Utilize Snowflake's data lineage feature in conjunction with object tagging. Tag relevant columns and tables, then query the lineage views to trace dependencies.
D) Use Snowflake's ACCOUNT USAGE views related to query history and object dependencies, combined with a custom script to recursively trace data lineage based on SQL operations (e.g., INSERT INTO ... SELECT).
E) Implement a custom data lineage tracking system by parsing all SQL queries executed in the Snowflake environment and storing the dependencies in a separate metadata database.

3. You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

A) Enable Kafka Connect's internal offset storage by configuring 'offset.storage.topic' and 'config.storage.topic'.
B) Set 'tasks.max' to a value greater than 1.
C) Utilize Snowflake's auto-ingest feature alongside the Kafka Connector.
D) Configure 'errors.tolerance' to 'all'.
E) Configure 'errors.deadletterqueue.topic.name' to specify a Dead Letter Queue (DLQ) topic.

4. Which of the following statements are TRUE regarding Snowflake's Fail-safe mechanism and its relation to Time Travel? (Select all that apply)

A) Fail-safe is automatically enabled for all Snowflake accounts and requires no configuration.
B) The Fail-safe period starts immediately after the Time Travel retention period ends.
C) Fail-safe provides a historical data retention period of 7 days, similar to the default Time Travel setting.
D) Fail-safe is exclusively used by Snowflake to recover data in the event of a catastrophic system failure, and users have no direct access.
E) Users can query data directly from Fail-safe using SQL commands if Time Travel is insufficient.

5. A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A) Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.
B) Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.
C) Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.
D) Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
E) Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.

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