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

1. You are using Snowpipe to load data from an AWS S3 bucket into Snowflake. The data files are compressed using GZIP and are being delivered frequently. You have observed that the pipe's backlog is increasing and data latency is becoming unacceptable. Which of the following actions could you take to improve Snowpipe's performance? (Select all that apply)

A) Ensure that the S3 event notifications are correctly configured and that there are no errors in the event delivery mechanism.
B) Check if the target table has any active clustering keys defined which could be causing slow down
C) Increase the virtual warehouse size associated with the pipe.
D) Reduce the number of columns in the target Snowflake table. Fewer columns reduce the overhead of data loading.
E) Optimize the file size of the data files in S3. Smaller files are processed faster by Snowpipe.

2. 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?

A)

B)

C)

D)

E)

3. You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

A) Enable Kafka Connect's internal offset storage by configuring 'offset.storage.topic' and 'config.storage.topic'.
B) Set 'tasks.max' to a value greater than 1.
C) Configure 'errors.tolerance' to 'all'.
D) Configure 'errors.deadletterqueue.topic.name' to specify a Dead Letter Queue (DLQ) topic.
E) Utilize Snowflake's auto-ingest feature alongside the Kafka Connector.

4. You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

A) The Snowpipe is paused due to exceeding the daily quota.
B) Snowflake does not support Avro format for Snowpipe.
C) The data files in cloud storage are not being automatically detected by Snowpipe.
D) The cloud storage event notifications are not properly configured to trigger Snowpipe.
E) The file format definition in Snowflake is incompatible with the Avro schema.

5. 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) 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.
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) 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.
D) 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.
E) You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.

Solutions: