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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
B) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
C) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.
D) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
E) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.

2. A data engineering team has implemented a continuous data pipeline that loads data into a Snowflake table named 'SALES DATA' They notice that the pipeline intermittently experiences performance degradation, particularly during peak business hours. The team wants to implement alerts to proactively identify and address these performance issues. Which of the following approaches would be MOST effective for monitoring the pipeline and triggering alerts based on specific performance metrics related to data loading?

A) Enable Snowflake's query acceleration service. This service automatically analyzes query performance and identifies opportunities for optimization, removing the need for manual monitoring and alerting. Use Snowflake's resource monitors to track credit usage.
B) Implement a data streaming service that monitors the 'SALES_DATX table in real-time. The streaming service should track the number of rows inserted per minute and trigger an alert if the insertion rate drops below a predefined threshold. No Snowflake object or Alert required.
C) create a custom Snowflake Alert that triggers when the function for the 'SALES_DATA' table indicates significant delay in data loading. Use a Snowflake Notification Integration to send alerts via email or Slack.
D) Create a Snowflake Alert based on a metric in the Account Usage views (e.g., that identifies when load durations for the data warehouse associated with the data pipeline exceed a specified threshold. Configure a Notification Integration to route alerts to a designated channel.
E) Create a Snowflake Task that periodically queries the 'QUERY_HISTORY view, calculates the average load duration for 'SALES_DATX, and triggers an alert if the duration exceeds a predefined threshold. Use a Stored Procedure to handle the alert logic and send notifications.

3. 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) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
C) 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.
D) 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.
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.

4. You are troubleshooting a slow-running query that joins a large fact table 'SALES DATA' (100 billion rows) with a smaller dimension table 'CUSTOMER DIM' (1 million rows) on 'CUSTOMER ID. Initial analysis shows that the query is spending significant time in the join operation. You suspect the issue lies with the join strategy being used by Snowflake. Which of the following actions are MOST likely to improve query performance and optimize the join?

A) Increase the virtual warehouse size and monitor for spillover to local disk. If spilling occurs, further increase the warehouse size.
B) Ensure both 'SALES DATA' and 'CUSTOMER DIM' are clustered on 'CUSTOMER ID.
C) Ensure that the 'CUSTOMER_ID column in both tables has compatible datatypes and that no implicit type conversions are happening during the join. Also check cardinality of 'CUSTOMER_ID in the SALES DATA table.
D) Analyze the query profile in Snowflake's web UI and identify if a broadcast join is occurring. If so, consider increasing session parameter (within limits) or re-designing the query to avoid the broadcast join.
E) Convert the query to use a LATERAL FLATTEN function to pre-process the 'CUSTOMER_DIW table before the join.

5. You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?

A) Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.
B) Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
C) Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF
D) Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.
E) Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.

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