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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q297-Q302):
NEW QUESTION # 297
You're building a data product on the Snowflake Marketplace that includes a view that aggregates data from a table containing Personally Identifiable Information (PII). You need to ensure that consumers of your data product CANNOT directly access the underlying PII data but can only see the aggregated results from the view. What is the MOST secure and recommended approach to achieve this?
- A. Grant the 'SELECT privilege directly on the underlying PII table to the share used for the Marketplace listing, along with the 'SELECT privilege on 'sensitive data view'.
- B. Grant USAGE privilege on the database containing the PII table and to the share.
- C. Grant 'READ privilege on the internal stage containing the data files backing the PII table.
- D. Grant the 'SELECT privilege only on the to the share used for the Marketplace listing. Do not grant any privileges on the underlying PII table.
- E. Create a stored procedure that returns the aggregated data, and grant EXECUTE privilege on the stored procedure to the share. The stored procedure SELECTs from the PII table.
Answer: D
Explanation:
Granting only 'SELECT privilege on the (option B) ensures that consumers can only access the view and not the underlying PII data. Granting 'SELECT on the underlying table (option A) defeats the purpose of the view. Using a stored procedure (option C), while potentially masking the data access, is less performant and can still expose data if not carefully implemented. 'USAGE privilege (option D) only allows access to the database, not the data itself. 'READ' on the stage (option E) allows direct access to the raw data, which exposes the PII.
NEW QUESTION # 298
Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)
- A. Once a masking policy is applied to a column, the original data is permanently altered.
- B. The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.
- C. Different masking policies cannot be applied to different columns within the same table.
- D. Data masking policies are supported on external tables.
- E. Data masking policies can be applied to both tables and views.
Answer: B,D,E
Explanation:
A and D are correct. Masking policies can be applied to tables and views, and the function is essential for implementing role-based masking. B is incorrect because masking policies apply dynamically at query time and don't alter the underlying data. C is incorrect; different policies can be applied to different columns. E is correct, Data masking policies are also supported on external tables.
NEW QUESTION # 299
You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?
- A. Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
- B. Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
- C. Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
- D. Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
- E. Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
Answer: D
Explanation:
Option D is generally the most efficient. Converting Avro to JSON before ingestion simplifies the integration with Snowpipe and Snowpark. Snowpipe is optimized for semi-structured data like JSON within a VARIANT column. Subsequently, Snowpark DataFrames can easily process the JSON data using built-in functions, avoiding the complexity and potential performance bottlenecks of UDFs (Option B) or custom connectors (Option A). Although Snowflake's function can work with variant data, operating on raw Avro data is not natively supported by Snowpipe without pre-processing or complex UDF logic. External functions (Option E) add another layer of complexity for data retrieval.
NEW QUESTION # 300
You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?
- A. Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
- B. Create an index on the 'ORDER DATE column.
- C. Increase the virtual warehouse size to 'LARGE or 'XLARGE.
- D. Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
- E. Redesign the dashboard to minimize the data being displayed at once to the user.
Answer: A,D
Explanation:
The ' EXPLAIN' output shows that the 'TableScan' operation has a high cardinality (100000000 rows), indicating that Snowflake is scanning a large portion of the table. The 'Filter' operation also has a high cardinality (1000000 rows). Creating a materialized view (A) that pre- aggregates the data by 'ORDER_DATE and 'REGION' can significantly improve performance by reducing the amount of data that needs to be scanned and aggregated at query time. Clustering the table on and 'REGION' (C) can also improve performance by organizing the data in a way that allows Snowflake to prune more data during the table scan. Indexes are not supported, increasing warehouse size provides more compute resources but is less effective than optimizing the data organization or query strategy. Redesigning dashboard can help with presentation layer but not at Database layer.
NEW QUESTION # 301
A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:
Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.
- A. The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
- B. The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
- C. The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
- D. The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.
- E. The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.
Answer: B,E
Explanation:
Options A and D are the most direct solutions for disk spilling. A undersized warehouse directly impacts available memory, leading to disk spilling. Increasing the warehouse size (option A) provides more memory for the operation. When data spill happens increasing the warehouse size is the primary action to take. Option D correctly addresses the root cause of the spill an overly large intermediate result set. Reducing the data volume before the join minimizes the memory required. Option B could improve query performance overall, but doesn't directly address disk spilling. Option C is incorrect, as Snowflake does not support manual indexes. Option E would improve the accuracy of the query optimizer's decisions, which could indirectly improve performance, but is less direct than options A and D.
NEW QUESTION # 302
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