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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

A) Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
B) Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
C) Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
D) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
E) Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.

2. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
B) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
C) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
D) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
E) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.

3. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
B) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
C) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
D) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
E) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.

4. You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

A) The Snowflake virtual warehouse associated with the pipe is undersized. Increase the warehouse size to improve ingestion performance.
B) Snowflake's internal metadata cache is out of sync. Run 'ALTER PIPE SALES PIPE to refresh the cache.
C) Snowpipe auto-ingest only supports CSV files. Convert your JSON data to CSV format before loading.
D) There is a backlog of files in the internal Snowflake queue waiting to be processed. Monitor the 'SYSTEM$PIPE STATUS' function and consider increasing the 'MAX CONCURRENCY' parameter (if applicable, based on underlying infrastructure considerations) on the pipe definition.
E) The S3 bucket is not in the same region as the Snowflake account. Ensure the S3 bucket and Snowflake account are in the same region to reduce network latency.

5. You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A) DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
B) The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
C) The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
D) The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
E) The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.

Solutions: