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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You are tasked with building a data pipeline that ingests data from various sources into Snowflake, processes it, and then writes the final results back to a data lake in AWS S3, partitioned by date. The data in S3 should be queryable by other applications outside of Snowflake. You choose to use Snowflake Iceberg tables for this purpose. Which of the following is the correct SQL statement to create an Iceberg table 'analytics.public.daily_summary' in Snowflake, backed by an S3 bucket 's3://your-bucket/data/daily_summary/', partitioned by the column, and specifying 'parquet' as the file format?

A. Option D
B. Option E
C. Option B
D. Option A
E. Option C

Question 2

Consider a scenario where you have a Snowflake table named 'CUSTOMER DATA' containing customer IDs (INTEGER) and encrypted credit card numbers (VARCHAR). You need to create a secure JavaScript UDF to decrypt these credit card numbers using a custom encryption key stored securely within Snowflake's internal stage, and then mask all but the last four digits of the decrypted number for data protection. Which of the following actions are necessary to ensure both functionality and security while adhering to Snowflake's best practices for UDF development and security?

A. Store the encryption key in a separate file on an internal stage accessible only by the UDF's service account and load the key from the file within the UDF at runtime.
B. Pass the encryption key as an argument to the UDF each time it is called.
C. Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
D. Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.
E. Store the encryption key directly within the JavaScript UDF code as a string variable.

Question 3

A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A. Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
B. Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
C. Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
D. Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
E. Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.

Question 4

You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

A. Load all files from the stage using 'COPY INTO' into a staging table, and then use a Snowflake task to filter and move the relevant records into the 'sensor_readingS table.
B. Use a Python UDF to iterate through the files listed in , filter based on filename, and then load each matching file individually using the Snowflake Python Connector.
C. Create a view on top of the directory table that filters the 'relative_patW based on the substring match, and then use 'COPY INTO' with the 'FILES' parameter to load the filtered files.
D. Create a masking policy based on filenames to control which files users can see.
E. Use 'COPY INTO' with the 'PATTERN' parameter, constructing a regular expression that includes the substring match against the filename obtained from the directory table's 'relative_path' column.

Question 5

A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

A. The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.
B. DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.
C. The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.
D. The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.
E. The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.

Solutions: