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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q308-Q313):

NEW QUESTION # 308
You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

A. Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
B. Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.
C. Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
D. Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
E. Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.

Answer: C

Explanation:
Implementing the retry logic within a stored procedure called by the task (B) provides the most controlled and efficient way to handle transient errors. The stored procedure can handle the retry attempts, waiting periods, and error handling without requiring manual intervention or significantly impacting the overall pipeline. A is less desirable because SYSTEM$WAIT is generally discouraged in task definitions. C relies on manual intervention and doesn't automate the retry. D doesn't address the need for retries. E is overly complex and unnecessary. It also defeats the purpose of using 'AFTER' keyword.

NEW QUESTION # 309
You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A. Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
B. Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
C. Create an index on the 'ORDER DATE column.
D. Increase the virtual warehouse size to 'LARGE or 'XLARGE.
E. Redesign the dashboard to minimize the data being displayed at once to the user.

Answer: A,B

Explanation:
The ' EXPLAIN' output shows that the 'TableScan' operation has a high cardinality (100000000 rows), indicating that Snowflake is scanning a large portion of the table. The 'Filter' operation also has a high cardinality (1000000 rows). Creating a materialized view (A) that pre- aggregates the data by 'ORDER_DATE and 'REGION' can significantly improve performance by reducing the amount of data that needs to be scanned and aggregated at query time. Clustering the table on and 'REGION' (C) can also improve performance by organizing the data in a way that allows Snowflake to prune more data during the table scan. Indexes are not supported, increasing warehouse size provides more compute resources but is less effective than optimizing the data organization or query strategy. Redesigning dashboard can help with presentation layer but not at Database layer.

NEW QUESTION # 310
You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

A. Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
B. Increase the warehouse size.
C. Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.
D. Enable search optimization directly on the 'EVENT DATA' column.
E. Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.

Answer: E

Explanation:
Extracting the frequently queried key-value pairs into separate virtual columns and then enabling search optimization on these columns is the most effective approach (Option B). Snowflake's search optimization works best on columns with well-defined data types. Direct search optimization on a JSON column (Option A) is not directly supported and will not provide the desired performance benefits. Materialized views (Option C) are an option, but virtual columns are generally more lightweight for this scenario. Converting to VARCHAR (Option D) is not the correct approach for JSON data and would prevent proper JSON parsing and filtering. Increasing the warehouse size (Option E) might improve overall performance but doesn't specifically address the JSON filtering bottleneck.

NEW QUESTION # 311
You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

Answer: B

Explanation:
Option D is the most efficient because it filters access based on roles in the RAP without applying expensive UDFs within the policy itself. This minimizes the performance impact of the RAP. The view 'analyst_view' then applies the masking/hashing for analysts. Options A and B apply the UDFs within the RAP, which will significantly degrade performance. The 'MASK EMAIL(email_address) IS NOT NULL' conditions are also incorrect as they are not validating the email. Option C doesn't implement the required masking/hashing for analysts at all, and also is not as effecient. Option E allows both roles to see all data which does not meet requirement 1.

NEW QUESTION # 312
You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

A. Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.
B. Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
C. Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.
D. Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.
E. Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.

Answer: A,C

Explanation:
Options A and C are the most suitable. Option A involves direct integration and option C introduces batching and serverless function to improve performance and manage authentication. Option B is incorrect because external functions cannot directly trigger data loading based on external stage events. Option D bypasses the REST API requirement and does not address authentication. Option E avoids the REST API entirely, which is against the problem requirement.

NEW QUESTION # 313
......

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