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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

A) Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
B) Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.
C) Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
D) Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'
E) Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.

2. You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

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

3. A data engineer is using the Snowflake Spark connector to write data to a Snowflake table. The write operation fails consistently with the error 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: String '. ' is too long (maximum is 16777216)'. Which of the following is the most likely cause and how can it be resolved using Spark Connector?

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

4. You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

A) Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
B) Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
C) Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
D) Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
E) Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.

5. You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?

A) Use 'SPLIT to split the key-value pairs into an array, then use a LATERAL FLATTEN to create rows from array, then use 'SPLIT again to split each row by '='. Finally, construct the JSON using 'OBJECT CONSTRUCT.
B) Utilize to split the key-value pairs into rows, then use 'REGEXP_EXTRACT to extract the key and value. Finally, use 'OBJECT_CONSTRUCT and to construct the JSON object.
C) Use only REGEXP EXTRACT ALL' with appropriate regular expressions to extract all keys and values into arrays, then use a JavaScript UDF to combine them into a JSON object.
D) Use a combination of 'SPLIT, 'REGEXP_REPLACE and 'OBJECT_CONSTRUCT within a user-defined function (UDF) to parse the string and build the JSON object.
E) Use a Java UDF that iterates through the string, splitting it based on commas and equals signs, and then constructs a JSON object using a JSON library.

Solutions: