DAA-C01熱門證照，DAA-C01熱門考題

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and Load Data	15–20%	- File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe
Topic 2: Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Topic 3: Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Topic 4: Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Topic 5: Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis
Topic 6: Perform Simple Data Transformations for Analysis	15–20%	- Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion
Topic 7: Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions

>> DAA-C01熱門證照 <<

DAA-C01熱門考題 - DAA-C01測試引擎

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最新的 SnowPro Advanced DAA-C01 免費考試真題 (Q25-Q30):

問題 #25
You are tasked with aggregating website clickstream data in Snowflake to identify the most popular product categories per region on a daily basis. The clickstream data is stored in a table named 'clickstream eventS with columns: 'event_time', 'user id', 'product id', 'region', and 'category'. You need to create a solution that efficiently identifies the top 3 categories for each region on each day. Which approach offers the best performance and scalability considering the dataset size is expected to grow significantly?

A. Using a combination of GROUP BY, DENSE_RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3. DENSE RANK handles ties more gracefully.
B. Implementing a stored procedure that iterates through each region and day, calculating the category counts and selecting the top 3 using procedural logic.
C. Using a simple GROUP BY operation to count the occurrences of each category, region, and day, then relying on external tools to filter the top 3.
D. Creating a series of temporary tables for each region, aggregating the data, and then using a JOIN operation to combine the results.
E. Using a combination of GROUP BY, RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3.

答案：A

解題說明：
Option D is the most efficient and scalable because it leverages Snowflake's built-in window functions (DENSE_RANK) for efficient ranking within partitions (region and day). Window functions are optimized for parallel processing. DENSE RANK handles ties appropriately (assigning the same rank to tied categories). Options A is similar but DENSE_RANK is better for the case of ties. Option B would be slow and not scalable due to the iterative nature of stored procedures. Option C is inefficient due to the creation of temporary tables and JOIN operations. Option E offloads the crucial filtering to external tools, impacting performance.

問題 #26
When implementing data processing solutions, how does aggregating and enriching data contribute to the overall data analysis process?

A. Increases data redundancy
B. Limits data correlation possibilities
C. Enhances data granularity
D. Slows down data retrieval speed

答案：C

解題說明：
Aggregating and enriching data enhances data granularity by providing more detailed and comprehensive insights into datasets, enabling better analysis and correlation of information.

問題 #27
In Snowflake, how does the implementation of primary keys for tables contribute to data integrity?

A. Only works on specific data types
B. Limits data access for users
C. Enforces uniqueness in table rows
D. Requires data redundancy

答案：C

解題說明：
Primary keys enforce uniqueness in table rows, ensuring data integrity by preventing duplicate entries within a table.

問題 #28
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?

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

答案：A

解題說明：
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.

問題 #29
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

A. They enable the execution of repetitive tasks, enhancing efficiency.
B. Stored procedures allow limited data accessibility for improved security.
C. Stored procedures hinder customization in data operations.
D. UDFs enhance query performance more effectively than stored procedures.

答案：A

解題說明：
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.

問題 #30
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