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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:

Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?

A) Use the 'functools.lru_cache' decorator to cache the results of the 'get_customer details' function within the UDF's scope. This will automatically cache the most recently used API responses.
B) Create a global dictionary within the UDF to store the API responses, using the customer ID as the key. Before calling the API, check if the customer ID exists in the dictionary; if it does, return the cached response. This approach will keep cached values during the session.
C) Leverage external caching services like Redis by making API calls to Redis from the UDF to store and retrieve cached API responses. This would require configuring Snowflake to connect with external systems.
D) Utilize Snowflake's built-in caching mechanisms (result caching) by ensuring the UDF is deterministic and only depends on its input parameters. Snowflake will automatically cache the results of the UDF for subsequent calls with the same input.
E) Persist the API responses in a temporary table within Snowflake. The UDF will first query the temporary table for the customer ID; if found, return the cached data. Otherwise, call the API and store the response in the temporary table for future use.

2. A Snowflake table, contains product information in a VARIANT column named This column holds JSON structures. You need to create a view, , that exposes specific fields , and 'category') as structured columns, and should gracefully handle scenarios where may contain characters incompatible with VARCHAR, 'category' is nested inside an array called 'tags'. What is the BEST and the MOST robust approach?

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

3. You are responsible for monitoring a critical data pipeline that loads data from an external Kafka topic into a Snowflake table 'ORDERS' Data anomalies have been frequently observed, impacting downstream reporting. You want to implement a solution that proactivelyidentifies and alerts on data quality issues such as missing values, invalid formats, and unexpected data distributions. Which combination of Snowflake features and approaches would be MOST effective for achieving this objective with minimal performance overhead on the pipeline itself?

A) Implementing custom SQL-based data quality checks within a scheduled Snowflake task that runs after the data load and writing results to an audit table for monitoring.
B) Creating a separate Snowflake pipeline that reads from the same Kafka topic, performs data quality checks in real-time using Snowpipe and streams the results to an alert system.
C) Employing Snowflake's built-in statistics and histogram features to analyze data distribution in the 'ORDERS' table and configure alerts based on deviations from historical patterns, combined with a Snowflake Native App for data quality reporting.
D) Using Snowflake's 'VALIDATE' table function after the data load to check for data corruption and then trigger alerts based on the validation results.
E) Leveraging Snowflake's Data Governance features along with Snowpark UDFs to define and enforce data quality rules at the time of ingestion using a Python- based library like Great Expectations, configured to trigger alerts through Snowflake Notifications.

4. A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A) Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
B) Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
C) Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
D) Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.
E) Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.

5. You are tasked with optimizing a continuous data pipeline that loads data from an external stage into a Snowflake table using streams.
The pipeline is experiencing significant latency during peak hours. The stream is defined on a very large table with frequent updates and deletes. Which of the following strategies would be MOST effective in reducing the latency of the data pipeline, considering stream performance and cost implications?

A) Increase the size of the virtual warehouse used for loading data. This will provide more compute resources for processing the stream.
B) Create multiple streams on the same base table, each filtering for specific types of changes (e.g., INSERT, UPDATE, DELETE).
C) Implement a more aggressive pruning strategy on the base table to reduce the amount of data that the stream needs to track.
D) Reduce the RETENTION TIME of the stream. This will limit the amount of historical data tracked and improve performance.
E) Implement a materialized view on top of the stream to pre-aggregate the data.

Solutions: