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Snowflake DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) Questions - With 25%

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Snowflake DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) Questions - With 25%

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q146-Q151):NEW QUESTION # 146
You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)
  • A. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
  • B. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
  • C. The file format used for the external table is incompatible with the data files in Blob Storage.
  • D. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
  • E. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
Answer: B,E
Explanation:
The two primary reasons for delays in Snowflake detecting new files in an external table are: 1) Incorrect configuration of the cloud provider's notification service (Azure Event Grid in this case). Snowflake relies on these notifications to be informed about new file arrivals. If the integration isn't set up correctly, Snowflake won't know when to refresh the metadata. 2) The parameter must be set to ' TRUE' for automatic metadata refresh to occur. If it's set to FALSE , manual refreshes are required using 'ALTER EXTERNAL TABLE ... REFRESH". Options D and E, although possible issues, won't directly cause a delay in detecting new files, but rather cause issues accessing files after detection. Option C is irrelevant as Snowflake's caching mechanism does not directly impact external table metadata refresh.

NEW QUESTION # 147
Consider the following Python Snowpark stored procedure designed to enrich customer data:

What are potential drawbacks or limitations of this approach, and what improvements can be made to enhance its scalability and maintainability? (Select all that apply)
  • A. Hardcoding the SQL query within the stored procedure makes it inflexible. Using Snowpark DataFrame operations instead would improve maintainability.
  • B. The code is perfectly fine and doesn't require any improvements, as it effectively enriches customer data using a UDF and Snowpark DataFrame operations.
  • C. Fetching data from an external API inside a UDF can introduce performance bottlenecks and potential network issues. Snowflake recommends minimizing external dependencies within UDFs.
  • D. Defining a UDF within the stored procedure creates unnecessary overhead as the UDF is recompiled every time the stored procedure runs. It's more efficient to define the UDF separately and call it from the procedure.
  • E. The operation retrieves all rows into the client's memory, which can be inefficient for large datasets. Consider using 'write' operation to store results to table.
Answer: A,C,D,E
Explanation:
Options A, B, C, and D are all valid drawbacks. Defining a UDF within a stored procedure can lead to recompilation overhead (A). Hardcoding SQL makes the procedure inflexible (B). External API calls within UDFs can cause performance issues (C). Using 'collect()' is inefficient for large datasets (D). Option E is incorrect as the code has several potential issues.

NEW QUESTION # 148
A data engineer is tasked with migrating data from a large on-premise Hadoop cluster to Snowflake using Spark. The Hadoop cluster contains nested JSON dat a. To optimize performance and minimize data transformation in Spark, what is the most efficient approach to read the JSON data into a Spark DataFrame and write it directly to a Snowflake table?
  • A. Read the JSON data as strings and utilize Snowflake's 'PARSE JSON' function within a Spark SQL query to transform and load the data into a variant column in Snowflake.
  • B. Use the 'STORAGE_INTEGRATION' feature in Snowflake to directly access the JSON files in Hadoop (via an external stage) and load the data without using Spark at all.
  • C. Read the JSON data as text files, then use Spark to parse and flatten the JSON structure before writing to Snowflake using the Snowflake JDBC connector.
  • D. Use the Snowflake Spark connector with the 'inferSchema' option set to 'true' when reading the JSON data. This allows Spark to automatically infer the schema and write directly to Snowflake.
  • E. Define a schema manually in Spark, then read the JSON data into a Spark DataFrame. Use the Snowflake Spark connector to write the data to Snowflake, specifying the schema explicitly.
Answer: E
Explanation:
Defining a schema manually in Spark provides the most control over the data types and avoids potential schema inference issues. While Snowflake can parse JSON directly (option E and D), using Spark allows for potential transformations and data cleaning before loading. Option B's 'inferSchema' can be slow and may not accurately represent complex nested structures. Option A forces unnecessary string parsing in Spark, and direct access via STORAGE INTEGRATION bypasses the request to use spark connectors.

NEW QUESTION # 149
You are tasked with building a User-Defined Aggregate Function (UDAF) in Snowflake to calculate the weighted average of product prices. The weight is determined by the quantity sold for each product. You have the following table: 'SALES (product_id INTEGER, price quantity INTEGER)'. Which of the following UDAF definitions would correctly calculate the weighted average?

  • A. Option B
  • B. Option E
  • C. Option D
  • D. Option C
  • E. Option A
Answer: C
Explanation:
Option D is the correct implementation of a UDAF for calculating the weighted average. It uses a struct to store the sum of (price quantity) and the sum of quantities. It correctly initializes, accumulates, merges, and finalizes the aggregate result. Other options have logical and syntactic errors making it fail. Option A is wrong as it does not provide aggregate function. Option B defines SQL UDF as aggregate functions are called when there are multiple rows, and a single result is desired from those rows, making this definition also wrong. Option C does not account for sum of quanities and uses an unprovided count. Option E also does not account for quanities.

NEW QUESTION # 150
Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?
  • A. Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.
  • B. Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
  • C. Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.
  • D. Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
  • E. Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
Answer: C,D,E
Explanation:
The correct answers are A, B, and C. Option A reduces the amount of data transferred over the network, improving performance and reducing costs. Option B minimizes data transfer by sending only necessary data. Option C improves throughput by processing more rows per Lambda invocation, potentially reducing overall execution time. Option D requires a binary format compatible with both Snowflake and Lambda, which can be complex to implement and may not always provide significant benefits. Option E could improve performance by executing directly within Snowflake, but requires re-writing the code and may not be feasible if the Python library relies on specific dependencies not available in the Snowflake Java UDF environment.

NEW QUESTION # 151
......
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