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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake Stream named 'PRODUCT CHANGES' created on a table 'PRODUCTS'. A downstream task attempts to consume records from the stream, but occasionally fails with a 'Table PRODUCTS has been altered' error. The 'PRODUCTS' table undergoes DDL changes (e.g., adding/dropping columns) infrequently, but these changes are necessary for evolving business requirements. How can you design a more resilient data pipeline that minimizes disruptions caused by DDL changes to the 'PRODUCTS' table while still leveraging the 'PRODUCT CHANGES' stream?

A) Implement error handling in the downstream task to automatically retry consuming records from the 'PRODUCT CHANGES' stream after a delay, assuming the DDL changes will be completed quickly.
B) Use a materialized view instead of a standard view as the source for the stream. Materialized views are less susceptible to issues when the underlying base table changes
C) Create a task that monitors the 'PRODUCTS' table for DDL changes using 'INFORMATION SCHEMA. TABLES'. When a change is detected, pause the downstream task, execute the DDL change, and then resume the downstream task after a short delay.
D) Before executing any DDL changes on the 'PRODUCTS' table, drop and recreate the 'PRODUCT CHANGES' stream. This ensures the stream definition is always in sync with the table structure.
E) Create a new Stream on the 'PRODUCTS' table after each DDL change. The downstream task should dynamically switch to consuming from the new stream when the old stream encounters an error.

2. You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A) Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.
B) JavaScript UDF utilizing regular expressions for simple string replacements.
C) Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
D) Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
E) SQL UDF with nested CASE statements for address standardization.

3. You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

A) Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
B) Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
C) Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
D) Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
E) Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.

4. You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

A)

B)

C)

D)

E)

5. Consider the following scenario: You are managing a Snowflake environment where users are running various queries with varying resource demands. You observe frequent warehouse resizing operations, leading to performance fluctuations and increased costs. Which of the following strategies, when implemented together, would BEST stabilize warehouse performance and minimize unnecessary resizing?

A) Enable Query Acceleration Service (QAS) for the warehouse. Set the warehouse size to Medium, regardless of the actual workload demands, and rely solely on QAS to handle performance variations.
B) Monitor query history using Snowflake's web interface and identify query patterns that consistently require larger resources; recommend users refactor those queries. Set the warehouse auto-suspend to a very short duration (e.g., 1 minute) to ensure resources are released quickly when idle.
C) Implement Query Tagging to categorize queries based on resource consumption. Analyze resource utilization patterns for different query categories. Adjust warehouse size and multi-cluster configuration based on these patterns, ensuring that a reasonable number of concurrent queries for each workload type is met.
D) Implement Resource Monitors to limit the daily credit consumption of the warehouse. Increase the warehouse size to accommodate all possible query demands and set the auto-suspend to a longer duration (e.g., 60 minutes).
E) Disable auto-suspend for the warehouse to prevent it from shutting down and causing performance delays. Force users to manually resize the warehouse as needed using SALTER WAREHOUSE commands.

Solutions: