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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

A)

B)

C)

D)

E)

2. You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

A)

B)

C)

D)

E)

3. You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A) Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
B) Granting the EXECUTE privilege on the UDF only to the roles that require access.
C) Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
D) Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.
E) Using masking policies instead of Secure UDFs is the recommended approach for data security

4. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
B) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
C) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
D) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
E) Create separate tables for each "PRODUCT_ID' to improve query performance.

5. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

Solutions: