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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

A)

B)

C)

D)

E)

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

3. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Contact Snowflake Support and request restoration from Fail-safe.
B) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
C) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
D) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
E) Use the 'UNDROP DATABASE PRODUCTION command.

4. You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

5. You are designing a data pipeline in Snowflake to process IoT sensor data'. The data arrives in JSON format, and you need to extract specific nested fields using a Snowpark UDF for performance reasons. Which of the following statements are true regarding best practices and limitations when working with complex JSON data and Snowpark UDFs (Python or Scala)? (Select all that apply)

A) The maximum size of the JSON document that can be processed by a Snowpark UDF is directly limited by the maximum size of the UDF code itself (typically a few MB), requiring chunking strategies for large JSON payloads.
B) For highly complex JSON structures, consider using a Scala UDF with a robust JSON parsing library like Jackson or Gson for potentially better performance and control over error handling compared to Python UDFs.
C) When working with Snowpark Python UDFs, it's recommended to use the 'json' module in Python to parse the JSON data within the UDF, as it's optimized for Snowflake's internal JSON representation.
D) Leverage Snowflake's built-in 'PARSE_JSON' function and 'GET_PATH' function outside of the UDF as much as possible before passing the data to the UDF to reduce the complexity within the UDF itself.
E) Ensure the UDF is idempotent, meaning it produces the same output for the same input, as Snowflake might execute UDFs multiple times for optimization purposes.

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