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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. 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.

2. Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?

A) Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.
B) Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
C) Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
D) Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.
E) Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.

3. A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?

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

4. A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A) Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.
B) Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
C) Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
D) Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
E) Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.

5. A Snowflake table 'ORDERS' contains billions of records and is frequently queried for reporting purposes. The reporting queries often filter on 'ORDER DATE and 'CUSTOMER ID'. The data engineering team is considering creating a clustering key to improve query performance. They are evaluating two options: (1) clustering on 'ORDER DATE' alone and (2) clustering on '(ORDER DATE, CUSTOMER ID)'. Which of the following statements best describes the trade-offs between these two options in the context of query performance and data maintenance?

A) Clustering on 'ORDER_DATE alone will result in better overall query performance because it's a single dimension, simplifying the clustering process and reducing the need for Snowflake to perform complex data scans.
B) Clustering on '(ORDER DATE, CUSTOMER IDY is always the best option because it allows for more granular filtering and reduces the need to scan unnecessary micro-partitions, regardless of data distribution.
C) Clustering on ' (ORDER DATE, CUSTOMER_ID)' will provide better performance for queries filtering on both columns, but may lead to increased reclustering costs if distribution is skewed within "ORDER_DATE' partitions.
D) Neither clustering option will significantly improve performance, and the team should focus on optimizing the queries themselves through techniques like query rewriting and the use of appropriate indexes.
E) Clustering on ORDER DATE alone is preferable because it eliminates the risk of data skewness associated with 'CUSTOMER ID, leading to more balanced micro-partitions and consistent query performance.

Solutions: