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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q162-Q167):

NEW QUESTION # 162
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. Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
B. 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.
C. Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
D. 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.
E. Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.

Answer: A,B,D

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 # 163
You need to load data from a stream of CSV files into a Snowflake table. The CSV files are delivered to an AWS S3 bucket and contain header rows. The files occasionally include records where a text field contains a delimiter character (comma) within the text itself, but these fields are properly enclosed within double quotes. You want to create a file format object that correctly handles the data, including quoted delimiters, and skips the header row. Which of the following file format options are required to achieve this? (Choose two)

A. FILE_FORMAT = (TYPE = CSV)
B. FIELD OPTIONALLY ENCLOSED BY =
C. SKIP HEADER = 1
D. ERROR ON COLUMN COUNT MISMATCH = FALSE
E. FIELD DELIMITER = ','

Answer: B,C

Explanation:
To correctly handle the CSV files with quoted delimiters and skip the header row, you need the following options: 'FIELD OPTIONALLY ENCLOSED BY ='''' specifies that fields can be optionally enclosed by double quotes, allowing delimiters within the text. 'SKIP HEADER = 1' instructs Snowflake to skip the first row (header row) in each file. Other options are implicit by default. A is already set by default, D is for creating a file format object (which it isn't), E is unrelated to skipping header

NEW QUESTION # 164
You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?

A. Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.
B. Write the Spark DataFrame to a temporary table in Snowflake. Then, execute an 'UPDATE statement in Snowflake joining the temporary table with the 'CUSTOMER table using the 'CUSTOMER_ID to update the 'NAME and 'ADDRESS' columns. Finally, drop the temporary table.
C. Iterate through each row in the Spark DataFrame and execute an individual 'UPDATE statement against the 'CUSTOMER table in Snowflake. Use the 'CUSTOMER_ID in the 'WHERE clause.
D. Write the Spark DataFrame to a temporary table in Snowflake using MERGE. Use the WHEN MATCHED clause for Update the target table based on updates from staging table and finally drop the staging table
E. Use Spark's foreachPartition to batch update statements and execute on each partition. This will help with efficient data transfer and avoid single row based updates.

Answer: B,D

Explanation:
Options A and D are the most efficient. Writing to a temporary table allows Snowflake to perform the update operation using its optimized internal processes. The MERGE command is designed for efficient upserts and is preferred over individual UPDATE statements. Option B is highly inefficient due to the overhead of multiple individual queries. Option C is also not optimal, as UDFs don't necessarily improve performance for simple UPDATE operations and broadcasting data is not needed. Option E can also be an approach for batch update, its effective way for the performance but its less compared with option A and D.

NEW QUESTION # 165
A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

A. Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.
B. Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.
C. Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
D. Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.
E. Create a data share and grant access to all consumers directly on the base tables.

Answer: C

Explanation:
Using time travel allows consumers to query a consistent snapshot of the data before the daily updates without requiring expensive data cloning or maintaining separate databases for each consumer. Cloning data daily (Options B and D) is extremely costly. Sharing the base tables directly (Option A) may not be desirable due to security or management considerations. Using external tables (option E) could introduce latency or require the consumer to manage their own compute resources.

NEW QUESTION # 166
A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

A. Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.
B. Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
C. Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
D. Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
E. Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.

Answer: A

Explanation:
Enabling search optimization on and in the 'SALES FACT table is the most effective approach for query acceleration in this scenario. This allows Snowflake to efficiently find the rows needed for the reports without scanning the entire table. Automatic Clustering improves data organization, but it doesn't directly accelerate individual queries in the same way. Materialized views are also useful, but require additional storage and maintenance. Simply increasing the warehouse size and enabling query acceleration without addressing the data organization or indexing might not be as cost-effective. Clustering dimension tables won't affect the performance on the large fact table.

NEW QUESTION # 167
......

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