Snowflake SnowPro Advanced DEA-C02 Braindumps

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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You are designing a Snowflake data pipeline that continuously ingests clickstream dat a. You need to monitor the pipeline for latency and throughput, and trigger notifications if these metrics fall outside acceptable ranges. Which of the following combinations of Snowflake features and techniques would be MOST effective for achieving this goal?

A. Use Snowflake's Event Tables and Event Notifications to capture events related to data ingestion and processing. Configure alerts based on event patterns that indicate latency or throughput issues.
B. Use Snowflake's 'QUERY_HISTORY view to track query execution times and implement a scheduled task that queries this view, calculates latency and throughput, and sends email notifications using Snowflake's built-in email integration if thresholds are exceeded.
C. Implement a combination of Snowflake Streams, Tasks, and external functions. Streams capture changes, Tasks process the changes, and external functions send notifications to a monitoring service when latency or throughput issues are detected.
D. Create a custom dashboard using a Bl tool that connects to Snowflake via JDBC/ODBC and visualizes data ingestion and processing metrics. Manually monitor the dashboard for anomalies.
E. Rely on Snowflake's default resource monitors to track warehouse usage. If warehouse usage exceeds a certain threshold, assume there are performance issues and send a notification.

Question 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. A and B
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. 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.
D. 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.
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.

Question 3

You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A. Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
B. Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
C. Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
D. Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
E. Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.

Question 4

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 C
B. Option B
C. Option A
D. Option E
E. Option D

Question 5

A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A. Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
B. Restore the entire Snowflake account to a previous point in time before the table was truncated.
C. Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
D. Contact Snowflake support and request them to restore the table from a system-level backup.
E. Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.

Solutions: