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1. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?
A) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
B) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
C) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
D) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
E) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
2. Consider the following Snowflake SQL API call to execute a stored procedure:
A) Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.
B) Set the parameter to and retrieve the result set directly from the API response.
C) Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.
D) The stored procedure should handle the error handling for network disruptions and automatically retry.
E) Use the parameter to specify which external functions are allowed to be called by the procedure.
3. A data engineering team is building a real-time data pipeline in Snowflake. Data arrives continuously and needs to be processed with minimal latency. The team is using Snowflake Streams and Tasks for incremental data processing. However, they are encountering issues where the tasks are sometimes skipped or delayed, leading to data inconsistencies. Which combination of actions would BEST address these issues and ensure reliable near real-time data processing?
A) Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.
B) Monitor the 'TASK HISTORY view regularly to identify skipped or delayed tasks and manually re-run them as needed. This is a reactive approach and does not prevent future occurrences.
C) Adjust the ' ERROR_INTEGRATION' parameter on the task definition to send notifications when tasks fail. This allows for manual intervention but does not prevent skipping.
D) Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
E) Configure the tasks to run using a serverless compute model (Snowflake-managed compute). Ensure the parameter is set to a higher value and implement error handling within the task using TRY/CATCH blocks.
4. You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?
A) Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
B) Avoid large or small number and stick to the limited range of input values.
C) Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
D) Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
E) Cast input arguments and the result to 'FLOAT within the UDF.
5. You are configuring cross-cloud replication for a Snowflake database named 'SALES DB' from an AWS (us-east-I) account to an Azure (eastus) account. You have already set up the necessary network policies and security integrations. However, replication is failing with the following error: 'Replication of database SALES DB failed due to insufficient privileges on object 'SALES DB.PUBLIC.ORDERS'.' What is the MOST LIKELY cause of this issue, and how would you resolve it? (Assume the replication group and target database exist).
A) The user account performing the replication does not have the 'ACCOUNTADMIN' role in the AWS account. Grant the 'ACCOUNTADMIN' role to the user.
B) The network policy is blocking access to the ORDERS table. Update the network policy to allow access to the ORDERS table.
C) The replication group is missing the 'ORDERS' table. Alter the replication group to include the 'ORDERS' table: 'ALTER REPLICATION GROUP ADD DATABASE SALES DB;'
D) The target Azure account does not have sufficient storage capacity. Increase the storage quota for the Azure account.
E) The replication group does not have the necessary permissions to access the 'ORDERS' table in the AWS account. Grant the 'OWNERSHIP' privilege on the 'ORDERS table to the replication group: 'GRANT OWNERSHIP ON TABLE SALES DB.PUBLIC.ORDERS TO REPLICATION GROUP
Solutions:
| Question # 1 Answer: C,E | Question # 2 Answer: A,B,C | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: E |
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