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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q56-Q61):

NEW QUESTION # 56
You are developing a data pipeline in Snowflake that processes sensitive customer data'. You need to implement robust data governance controls, including column-level security and data masking. Which of the following combinations of Snowflake features, when used together, provides the MOST comprehensive solution for achieving this?

A. Row-level security policies and data masking policies.
B. Row access policies and data masking policies on base tables, supplemented with object tagging and column-level security policies on views that grant limited access to specific user roles.
C. Data masking policies and network policies.
D. Dynamic tables and masking policies.
E. Object tagging, column-level security policies (using views), and masking policies.

Answer: B,E

Explanation:
Option E provides the most comprehensive solution. Here's why: Row access policies and data masking policies: These provide strong data protection at the row and column level. Using them on the base tables ensures that data is protected from the start. Object Tagging: Used to classify the sensitive data. Tagging sensitive data and then applying masking and Row level Policies Column-level security on views: Using views to grant access and using security policies on views protects the columns that should be accessed by each user Option B Provides column level security via views. Options A, C and D are incomplete solutions: A: Row-level security without column-level and data masking. C: Data masking without row-level and column-level security, also the masking does not protect against unauthorized access. D: Dynamic tables do not directly contribute to data governance controls.

NEW QUESTION # 57
Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

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

Answer: B

Explanation:
Option B is the most accurate and complete. It explicitly states that the JAR needs to be uploaded first, and then correctly shows the 'CREATE FUNCTION' syntax including 'IMPORTS', 'HANDLER, and specifying the language as 'JAVA". The handler specifies the method to be invoked inside the class. The execution follows the correct order. Option D is incorrect because HANDLER needs full qualified method name instead of the class name. And option E just ensures stage is present and jar file is there, rather explicitly mention upload.

NEW QUESTION # 58
A Snowflake table 'PRODUCT REVIEWS' is being ingested into from an external system. You have a stream 'PRODUCT REVIEWS STREAM' defined on this table to capture changes. Due to a bug in the ingestion process, incorrect data was loaded for a specific period. You need to correct the data'. Which of the following SQL statements, when executed against the 'PRODUCT REVIEWS STREAM' , will return the number of rows that were inserted, updated, and deleted during that period?

Answer: B

Explanation:
Option D is correct: The query 'SELECT METADATA$ACTION, count( ) FROM GROUP BY METADATA$ACTION$ will correctly return the number of rows for each action (INSERT, UPDATE, DELETE) within the stream. Options A, B, and C will give a total of only a single action type and E would give the total number of records in the stream regardless of action, failing to provide granular count per action.

NEW QUESTION # 59
You have a large dataset stored in AWS S3 in Parquet format. The data is constantly updated by an external process, but you need to run read-only analytical queries against the most current data in Snowflake without ingesting it. Which approach is the MOST efficient and cost-effective way to achieve this, considering minimal latency for query results?

A. Create an external table in Snowflake pointing to the S3 location. Configure automatic data refresh using the 'AUTO REFRESH' parameter and a notification integration.
B. Create a Snowflake internal stage, load the Parquet files into the stage periodically, and create a table from the staged files.
C. Use Snowpipe to continuously ingest the Parquet files from S3 into a Snowflake table.
D. Create a Snowflake external table pointing to the S3 location. Manually refresh the external table metadata periodically using the 'ALTER EXTERNAL TABLE REFRESH' command.
E. Create a view on top of the S3 files using a custom Java UDF to read Parquet data directly. Then, query the view.

Answer: A

Explanation:
Option B is the most efficient. External tables allow querying data directly from S3 without ingestion. 'AUTO_REFRESH' and notification integration ensures the metadata is automatically updated when new files are added to S3, providing near real-time access. Options A and D involve data ingestion, which adds latency and storage costs. Option C requires manual intervention, and Option E using a Java UDF for Parquet reading is inefficient.

NEW QUESTION # 60
You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

A. Implement a Snowpark Python UDF that consumes data directly from the Kafka topic using a Kafka client library. Write data into 'STREAMED_DATX within a single transaction. Use a structured data type for the 'STREAMED DATA'.
B. Use Snowflake's native Kafka connector to load data into a staging table. Then, use a Task and Stream combination, using a Snowpark Python UDF, to transform and load the data into 'STREAMED DATA' within a single transaction, handling schema evolution by casting columns to their new types or dropping missing column data.
C. Use Snowpipe with auto-ingest and configure it to trigger on Kafka topic events. Define a VARIANT column in 'STREAMED_DATX to handle schema evolution.
D. Utilize Snowflake Streams on in conjunction with Snowpark to transform and cleanse the data after it has been ingested by Snowpipe. Apply a merge statement to update an external table of parquet files.
E. Use Snowflake Connector for Kafka to load data into a staging table. Then, use Snowpark Python to transform and load the data into 'STREAMED_DATR within a single transaction. Implement schema evolution logic in the Snowpark code to handle changes in the Kafka topic schema.

Answer: B,E

Explanation:
Options D and E represent the most reliable solutions to this problem statement. Option D: The combination of the Snowflake Connector for Kafka and Snowpark offers a balanced approach. The connector efficiently loads the raw data, and Snowpark Python provides the flexibility to transform the data within a transaction and implement schema evolution logic. Option E: Snowflake's Kafka connector, combined with tasks, streams, and a Snowpark IJDF, provides a pipeline that continuously transforms data and is only triggered by new events in the staging table created by the Kafka connector. Implementing schema evolution in the IJDF itself handles small changes effectively. Option A does not provide exactly-once semantics. While VARIANT columns handle schema evolution, Snowpipe itself might deliver messages more than once. Option B is less scalable and harder to manage compared to using the Snowflake Connector for Kafka or Streams/Tasks. Option C, using Streams on 'STREAMED_DATA' , can lead to data duplication if not managed correctly and updating an external table negates a central table stream for change control.

NEW QUESTION # 61
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