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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A) Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
B) Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.
C) Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
D) Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
E) Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.

2. You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?

A) Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.
B) Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.
C) Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.
D) Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.
E) Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.

3. 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) 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.
C) Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
D) Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
E) Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.

4. You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

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

5. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
B) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
C) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
D) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
E) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.

Solutions: