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

1. A financial services company is using Snowflake Streams on a table 'TRANSACTIONS' to capture changes for auditing purposes. The 'TRANSACTIONS' table contains sensitive data, and the auditing team requires the stream to only capture changes to specific columns: 'ACCOUNT ID', 'TRANSACTION DATE', and 'TRANSACTION AMOUNT'. Which of the following approaches is the MOST efficient and secure way to achieve this requirement, ensuring minimal performance impact and data exposure?

A) Create a standard Stream on the 'TRANSACTIONS table and then filter the results in downstream processing to only include the required columns.
B) Create a Stream on the 'TRANSACTIONS' table. Periodically truncate stream and reload all data from TRANSACTION table by applying filter while loading.
C) Create a View that selects only the 'ACCOUNT ID, 'TRANSACTION DATE, and 'TRANSACTION AMOUNT columns and create a Stream on the View.
D) Create a task that clones the TRANSACTIONS table and a stream on that cloned table, limiting what changes are captured using a WHERE clause on the cloning command.
E) Create a Stream on the 'TRANSACTIONS' table and use a masking policy on the stream's output to redact the unnecessary columns.

2. You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

A) Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.
B) Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
C) A and B
D) Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
E) Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.

3. You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

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

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

5. You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)

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

Solutions: