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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q164-Q169):

NEW QUESTION # 164
You are designing a Snowflake alert system for a data pipeline that loads data into a table named 'ORDERS'. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

A. Use Snowflake's Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.
B. Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the 'ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.
C. You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.
D. Create a Snowflake Stream on the 'ORDERS' table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.
E. Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using 'SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.

Answer: B

Explanation:
Option D directly addresses the requirements by creating a Snowflake Alert that monitors the row count within the last hour and triggers when the threshold is breached. The alert can then be integrated with a notification system for timely alerts. Option A is incorrect as Snowflake supports alert conditions using rolling windows via SQL functions (e.g., "last_altereff). Option B describes a task-based solution, not using the Snowflake alert object directly. Option C is incorrect because streams track changes, not the total number of rows loaded within a period. Option E is an indirect method and does not precisely measure rows inserted; therefore, it does not satisfy the requirement of alerting based on the number of rows loaded.

NEW QUESTION # 165
You are tasked with creating an external function in Snowflake that calls a REST API. The API requires a bearer token for authentication, and the function needs to handle potential network errors and API rate limiting. Which of the following code snippets demonstrates the BEST practices for defining and securing this external function, including error handling?

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

Answer: E

Explanation:
Option A uses SECURITY_INTEGRATION, which is suitable for cloud provider-managed security but doesn't directly handle the API key. Option B uses CREDENTIAL, which is deprecated. Option C and D use AUTH POLICY and SECRET, but C doesn't use SYSTEM$GET_SECRET within a 'USING' clause or CONTEXT_HEADERS. Option D uses the 'USING' clause but does not use 'CONTEXT HEADERS to pass the token correctly. Option E is the BEST approach because it utilizes 'SECURITY INTEGRATION' along with 'CONTEXT_HEADERS' to pass the Bearer token securely retrieved from the Snowflake secret, ensuring proper authentication. Using CONTEXT HEADERS allows setting the authorization header directly. Also, its importand to create the 'SECRET api_secret' for this code to work correctly and this options uses it.

NEW QUESTION # 166
A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A. Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.
B. Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
C. Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.
D. Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.
E. Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.

Answer: D

Explanation:
External tables are ideal for infrequent queries of data residing in external storage. The 'EVENT_TABLE object provides an automatic refresh, but requires the setup of a notification service to trigger the refresh when new files are written to S3. Manually refreshing the metadata with "ALTER EXTERNAL TABLE ... REFRESH' is also a valid approach, but requires manual intervention or scheduling. Data freshness is NOT automatically maintained; a metadata refresh is REQUIRED.

NEW QUESTION # 167
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. 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.
B. Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.
C. Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
D. 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.
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.

Answer: E

Explanation:
Option C is the best solution. Serverless compute allows Snowflake to automatically manage resources for the tasks, ensuring they are not skipped due to insufficient compute. Setting 'SUSPEND TASK AFTER NUM FAILURES' avoids immediate suspension after a transient failure, and TRY/CATCH allows for robust error handling. Increasing warehouse size (A) may help, but serverless provides better elasticity. B only provides notification. D is incorrect as disabling tasks removes automation. E is a reactive approach.

NEW QUESTION # 168
A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?

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

Answer: A,C

Explanation:
Explanation: Option B identifies a key cause: timezone mismatches between Spark and Snowflake. Ensuring these are aligned is essential. Option D proposes converting 'TIMESTAMP NTZ to strings using 'sfTimestampNTZAsString' is the correct approach to preserve precise values, and setting the Spark session timezone is also critical. Option A is incorrect since there are parameters. Option C involves unnecessary data transformation within Snowflake which is what we are trying to avoid with the connector. Setting 'sfTimestampNTZAsString' to false causes conversion which lead to data issue.

NEW QUESTION # 169
......

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First-hand Snowflake DEA-C02 Exam Topic: SnowPro Advanced: Data Engineer (DEA-C02) | DEA-C02 Unlimited Exam Practice

Quiz 2025 The Best Snowflake DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Exam Topic

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q164-Q169):

Useful DEA-C02 Exam Topic & Leading Offer in Qualification Exams & Realistic Snowflake SnowPro Advanced: Data Engineer (DEA-C02)

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