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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are building a data pipeline to ingest JSON data from an external API using the Snowflake SQL API. The API returns nested JSON structures, and you need to extract specific fields and load them into a Snowflake table with a flattened schem a. You also need to handle potential schema variations and missing fields in the JSON data. Which approach provides the MOST robust and flexible solution for this scenario, maximizing data quality and minimizing manual intervention?

A) Load the raw JSON data into a VARIANT column in Snowflake. Create a series of views on top of the VARIANT column to extract the required fields and handle schema variations using 'TRY TO ' functions.
B) Utilize Snowflake's schema detection feature during the COPY INTO process. This will automatically infer the schema from the JSON data and create the table accordingly.
C) Parse the JSON data in your client application (e.g., Python) using a library like 'json' or , transform the data into a tabular format, and then use the Snowflake Connector for Python to load the data into Snowflake.
D) Use a stored procedure with dynamic SQL to parse the JSON, create new tables based on the current schema, and load data. Maintain metadata on table versions.
E) Use the 'JSON TABLE function in a Snowflake SQL query executed via the SQLAPI to flatten the JSON data and extract the required fields. Handle missing fields by using 'DEFAULT values in the table schema.

2. You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

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

3. You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A) Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
B) Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
C) Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
D) Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
E) Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.

4. You are tasked with optimizing the performance of a Snowflake virtual warehouse used for running several types of queries: short- running analytical queries with strict latency requirements, long-running batch data transformations, and ad-hoc queries from data scientists. The workload is unpredictable, and the team wants to minimize queueing and maximize resource utilization. Which warehouse configuration would be MOST appropriate to handle this mixed workload, minimizing cost and maximizing performance?

A) Three separate warehouses: a Medium warehouse for analytical queries, a Large warehouse for batch transformations, and an X-Small warehouse for ad-hoc queries.
B) A multi-cluster warehouse with a scaling policy of 'Economy' and a minimum of 1 and maximum of 2 clusters with auto-suspend set to 5 minutes.
C) A multi-cluster warehouse with a scaling policy of 'Standard' and a minimum of 1 and maximum of 3 clusters with auto-suspend set to 10 minutes.
D) A single X-Large warehouse with auto-suspend set to 5 minutes.
E) A single Small warehouse with auto-suspend set to 60 minutes.

5. You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A) Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
B) Increase the number of Snowflake virtual warehouses to handle the increased load.
C) Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
D) Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
E) Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.

Solutions: