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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q150-Q155):

NEW QUESTION # 150
A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A. Increase the warehouse size to accelerate the re-clustering process.
B. Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).
C. Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.
D. Drop the clustering key altogether to avoid re-clustering costs.
E. Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.

Answer: B,E

Explanation:
Options B and E are the best choices. Option B reduces the re-clustering frequency at off-peak hours and optimizes costs. Option E pre-sorts the data before loading, which leads to better clustering during initial data load. Clustering would be more optimal since the data is ingested into the final table. Option A only speeds up the process but doesn't fundamentally address the root cause (out-of-order data). Option C would hurt query performance, since queries are based on order. Option D, partitioning is not a native concept in snowflake, and is often confused with clustering. If the table has a partition expression, then then the pruning could improve performance. However in the context of problem of reducing the cost with frequent reclustering from out of order ingestion, it would not reduce the cost since reclustering would be same.

NEW QUESTION # 151
You have implemented external tokenization for a sensitive data column in Snowflake using a UDF that calls an external API. After some time, you discover that the external tokenization service is experiencing intermittent outages, causing queries using the tokenized column to fail. What is the BEST approach to mitigate this issue and maintain data availability while minimizing the risk of exposing the raw data?

A. Implement a try-catch block within the UDF. In the catch block, return a pre-defined static token value (same value always) instead of attempting to call the external tokenization service. You can't return the raw value.
B. Implement a masking policy on the column that returns the raw data when the tokenization UDF is unavailable, detected by catching exceptions within the policy logic.
C. Modify the tokenization UDF to cache tokenization mappings locally within the Snowflake environment. When the external service is unavailable, the UDF can use the cached values.
D. Implement a try-catch block within the UDF. In the catch block, return a pre-defined, non-sensitive default value instead of attempting to call the external tokenization service. You can't return the raw value.
E. Replicate the tokenized table to another Snowflake region and switch to the replica during outages of the primary region. The tokenization service is guaranteed to be available in at least one region.

Answer: D

Explanation:
Returning the raw data (option A) defeats the purpose of tokenization. Caching tokenization mappings locally (option C) introduces security risks and potential data synchronization issues. Replicating the table (option B) doesn't solve the immediate problem of the tokenization service outage; it only addresses regional disaster recovery. Returning a default, non-sensitive value (option D) maintains data integrity and avoids exposing sensitive data during outages. Returning the same static token (Option E) for all values could cause data corruption.

NEW QUESTION # 152
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
B. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
C. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
D. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
E. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.

Answer: B

Explanation:
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention

NEW QUESTION # 153
You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

Answer: C,E

Explanation:
Options D and E correctly extract the required data while handling potential null values and type conversions. Option D uses TRY_TO_VARCHAR which returns NULL if the cast fails or the value is missing. Option E uses ' IFF in conjunction with ' IS_ARRAY , 'ARRAY SIZE', and 'IS OBJECT to check for the existence and validity of the 'addresses' array and 'preferences' object, and then uses TRY_TO_VARCHAR for safe type conversion, making it very robust. Option A will throw an error if the 'addresses' array or 'preferences' object is missing. B is wrong as its where condition will filter the results. C does not handle null and type conversion.

NEW QUESTION # 154
You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:

When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?

A. Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
B. Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
C. Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
D. Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
E. Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.

Answer: A,B,C

Explanation:
Options A, C and E can all contribute to better performance. SQL UDFs benefit from Snowflake's optimized execution engine for standard operations, making them often faster than JavaScript UDFs for string manipulation when possible. Pre-compiling regular expressions (Option C) avoids redundant compilation steps during each UDF invocation. Converting to a Java UDF (Option E) gives more control over efficiency compared to JS. The option D may help, but the performance gain is not guaranteed and is more related to resource availability than the UDF's efficiency. The option B is not valid since the size of input STRING won't matter the javascript engine.

NEW QUESTION # 155
......

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