Valid DAA-C01 Test Syllabus - DAA-C01 New Study Guide

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q230-Q235):

NEW QUESTION # 230
You have a stored procedure written in Python within Snowflake that needs to process a large dataset. The procedure's performance is critical, and you want to optimize it for speed. Which of the following strategies would be MOST effective in improving the performance of your Python stored procedure when dealing with large datasets in Snowflake? (Select TWO)

A. Using the Snowflake Python Connector's 'execute_stream' method to stream data in chunks instead of loading the entire dataset into memory at once. This reduces memory consumption and improves performance, especially for very large datasets.
B. Increasing the warehouse size for the Snowflake account where the stored procedure is running. A larger warehouse provides more compute resources, which can speed up data processing tasks, including Python stored procedures.
C. Leveraging Pandas DataFrames within the Python stored procedure to perform data manipulation tasks in a vectorized manner. Pandas provides efficient data structures and functions for data processing, which can significantly improve performance compared to using standard Python loops.
D. Replacing all Python loops with recursive SQL queries to improve performance
E. Converting the Python stored procedure to a SQL stored procedure, as SQL is generally faster for data manipulation tasks within Snowflake due to its optimized query engine.

Answer: A,C

Explanation:
Options A and C are the most effective strategies. Option A, 'execute_stream' , is ideal for large datasets as it avoids loading everything into memory. Option C, leveraging Pandas DataFrames for vectorized operations, significantly speeds up data manipulation within Python. Option B is partially correct. SQL is often faster, but rewriting a complex Python procedure in SQL can be time-consuming and may not always be feasible or optimal. Option D, increasing the warehouse size, can help, but it's more of a general performance improvement rather than a specific optimization for Python stored procedures. Also it will cost more. E is incorrect as recursive SQL queries, while sometimes useful, aren't a general-purpose replacement for Python loops and can be less efficient for complex logic.

NEW QUESTION # 231
You are tasked with cleaning a dataset containing customer information. One of the columns, 'PHONE NUMBER', contains inconsistent data: some entries have leading country codes (+1), some have dashes, and others have no separators at all. You need to create a function that standardizes all phone numbers to a consistent format (e.g., '1234567890'). Which of the following Snowflake UDFs (User- Defined Functions) effectively cleans and standardizes the phone numbers, removing all non-numeric characters and country codes?

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

Answer: A,D

Explanation:
Option A and E are correct. Both use REGEXP REPLACE to remove any non-numeric characters (to-9]+). Then, they use REGEXP_REPLACE again to remove the leading country code (+1 or 1). The other options are either too simplistic (only handling specific characters) or don't fully remove the country code reliably. Option A removes leading '1+' and Option E removes '+1'. The '+ sign needs to be escaped in the regex pattern.

NEW QUESTION # 232
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

A. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
B. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
C. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
D. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
E. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.

Answer: A

Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.

NEW QUESTION # 233
You are working with a large dataset of website clickstream data'. You need to perform several data transformation steps, including filtering, aggregating, and joining with other tables. You want to ensure that your data cleaning and transformation process is idempotent, meaning that running the same transformation pipeline multiple times will produce the same result, even if the input data changes slightly. Which of the following strategies contribute to building an idempotent data transformation pipeline in Snowflake? (Select all that apply)

A. Using 'MERGE statements with appropriate matching conditions to update or insert records based on the latest data.
B. Always truncating the target table before inserting transformed data.
C. Relying solely on 'INSERT INTO' statements for data loading.
D. Using clones of source tables as input to the transformation pipeline, ensuring that the source data remains unchanged during the process.
E. Using temporary tables instead of permanent tables for transformation.

Answer: A,B,D

Explanation:
Options A, B, and D are correct. Truncating the target table (A) before each run ensures a clean slate. Using 'MERGE statements (B) handles both updates and inserts, ensuring that the target table reflects the latest data state regardless of previous runs. Using clones (D) protects the original source data from unintended modifications during the transformation process, contributing to idempotency. Option C using just INSERT INTO is not idempotent as it will duplicate data on rerun. Option E, while useful for performance and cleanup, doesn't directly contribute to idempotency. The use of temporary tables alone does not guarantee idempotency if the operations performed on them are not idempotent. You might still see inconsistencies in data if the operations are not carefully designed. Truncating permanent table before adding data using MERGE is more reliable and ensures consistent result even on multiple runs.

NEW QUESTION # 234
You're working with a Snowflake database containing sales transaction data'. The 'SALES' table includes columns 'transaction id' , product id', 'customer id', 'transaction_date', and 'sales amount'. The Bl team needs to analyze sales trends, identify top-selling products, and segment customers based on their purchase behavior. They also need to support ad-hoc queries with various filtering and aggregation criteria'. The data volume is significant, and query performance is a key concern. Consider the following Snowflake table definition (simplified):

Which of the following SQL queries, combined with appropriate data modeling techniques, will provide the BEST performance for analyzing monthly sales trends by product category, assuming a separate 'PRODUCTS table exists with 'product_id' and 'category' columns?

Answer: D

Explanation:
Option B provides the best performance because it uses a materialized view to pre- compute the monthly sales trends by product category. Subsequent queries to the materialized view will be significantly faster than re-calculating the aggregation each time. Option A performs the join and aggregation every time the query is executed. Option C uses a correlated subquery, which is generally less performant than a join. Option D uses , which might provide the same result but doesn't address the performance issue of recalculating the aggregation every time. Option E uses a temporary table which is unncessary because a materialized view can be created directly from the SALES and PRODUCTS table.

NEW QUESTION # 235
......

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VALID DAA-C01 TEST SYLLABUS - DAA-C01 NEW STUDY GUIDE

Valid DAA-C01 Test Syllabus - DAA-C01 New Study Guide