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

1. You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

2. You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?

A) Use Time Travel to clone the "TRANSACTIONS' table to a point in time one year ago. Then, export the cloned table to S3 and drop the cloned table. Delete the archived data from the 'TRANSACTIONS table.
B) Create a new table 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data, and then delete the archived data from the 'TRANSACTIONS table.
C) Partition the 'TRANSACTIONS table by date. Export the old partitions of the 'TRANSACTIONS' table to S3 using COPY INTO. Then, drop the old partitions from the 'TRANSACTIONS table and create an external table that points to the data in S3.
D) Create an external table pointing to S3. Then create new table named 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data from 'TRANSACTIONS' table into 'TRANSACTIONS ARCHIVE, and then delete the archived data from the 'TRANSACTIONS' table.
E) Export the historical data to S3 using COPY INTO, truncate the 'TRANSACTIONS' table, and then create an external table pointing to the archived data in S3.

3. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

4. You are designing a data loading process for a high-volume streaming data source. The data arrives as Avro files in an AWS S3 bucket. You need to load this data into a Snowflake table with minimal latency and operational overhead. Which of the following combinations of Snowflake features and configurations would be MOST suitable for this scenario? (Select TWO)

A) Use a Kafka connector to stream data directly from the Kafka topic to Snowflake.
B) Configure an external table pointing to the S3 bucket and query the Avro files directly from Snowflake.
C) Create a custom Spark application that reads Avro files from S3, transforms the data, and then writes it to Snowflake using the Snowflake Spark connector.
D) Use the 'COPY INTO' command with a scheduled task that runs every 5 minutes to load new files from the S3 bucket.
E) Implement Snowpipe with auto-ingest configured to listen for S3 event notifications whenever a new Avro file is added to the bucket.

5. You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A) Increase the virtual warehouse size associated with the pipe.
B) Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.
C) Check if the target table has any active clustering keys defined which could be causing slow down
D) Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.
E) Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.

Solutions: