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Snowflake DAA-C01 Valid Test Camp, DAA-C01 Reliable Exam Guide

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
A large retail company is migrating its transaction data to Snowflake and wants to build a consumption layer for BI reporting. They have historical data with frequent updates and require both point-in-time analysis and trend analysis. Which modeling technique(s) would be MOST suitable for this scenario, considering performance, storage efficiency, and the need for both historical tracking and current state views?

A. Data Vault modeling, with point-in-time tables built on top of the Data Vault for Bl reporting.
B. Star Schema with Slowly Changing Dimension Type 1 (SCDI) for all dimensions.
C. A wide, denormalized table created using CREATE TABLE AS SELECT (CTAS) statement, refreshed nightly.
D. Snowflake Schema with Slowly Changing Dimension Type 2 (SCD2) for relevant dimensions.
E. Star Schema with Slowly Changing Dimension Type 0 (SCDO) for critical dimensions.

Answer: A,D

Explanation:
Data Vault provides the historical tracking and auditability needed, while point-in-time tables on top optimize for BI queries. Snowflake schema with SCD2 also allows historical tracking within a dimensional model. SCDI alone will not preserve history, and SCDO locks in attributes indefinitely. A wide denormalized table is not suitable for historical analysis or frequent updates and can lead to redundancy. Therefore options B and C are suitable for BI reporting.

NEW QUESTION # 16
You have a Snowflake table named 'CUSTOMER DATA' with a 'JSON DATA' column containing nested JSON objects. You need to extract specific fields from the nested JSON, transform the data, and load it into a new table named 'CLEANED CUSTOMERS'. You want to automate this process using a task and stream. Which of the following SQL statements, when combined correctly, provide the most efficient and reliable solution for automating this data transformation?

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

Answer: A

Explanation:
The correct answer (D) uses a stream to capture changes in the 'CUSTOMER_DATX table. The task is chained to the stream using 'AFTER ensuring it runs only when there are changes in the stream. It then inserts the transformed data (name, age) from the stream into 'CLEANED_CUSTOMERS' , filtering based on Options A and C don't leverage streams and will re-process the entire 'CUSTOMER DATA table each time the task runs. Option B uses a MERGE statement without considering any audit columns from the stream and option E sets SHOW INITIAL ROWS = TRUE on stream that are deprecated and doesn't ensure task execution correctly because task doesn't use the stream state properly. A stream is crucial for incremental loading and efficient processing of only the changed data.

NEW QUESTION # 17
A data analyst is tasked with optimizing query performance for a frequently accessed report that calculates daily sales totals for different product categories. The report currently takes an unacceptable amount of time to generate. The underlying table 'SALES DATA contains columns: 'ORDER DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), (NUMBER). Which of the following database objects would be MOST effective to significantly improve the report's performance without requiring changes to the existing SQL query used for the report?

A. A materialized view created using the same SQL query as the report.
B. A standard view created using the same SQL query as the report.
C. A sequence object to pre-calculate sales amounts.
D. A temporary table created before running the report.
E. An external table pointing to the same data source as 'SALES_DATR.

Answer: A

Explanation:
A materialized view stores the results of the query, so subsequent queries can retrieve the pre-computed results directly. This avoids the need to re-run the query each time the report is generated, significantly improving performance. A standard view is just a stored query and doesn't store data. An external table is used for data residing outside of Snowflake. A temporary table would only be useful during the session it's created in and would need to be populated, adding to processing time. A sequence object is used for generating unique numbers, not for calculating sales amounts.

NEW QUESTION # 18
Which of the following is a key step in data preparation?

A. Data normalization
B. Visual analysis
C. Model deployment
D. Algorithm selection

Answer: A

NEW QUESTION # 19
When automating data processing, what significance do logging and monitoring solutions hold in ensuring seamless operations?

A. They restrict access to processed data.
B. They solely monitor system performance.
C. These solutions have no impact on data processing.
D. Logging and monitoring solutions aid in identifying processing bottlenecks.

Answer: D

Explanation:
Logging and monitoring solutions help identify processing bottlenecks, ensuring seamless operations in automated data processing.

NEW QUESTION # 20
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