Snowflake DAA-C01 Actual Test | DAA-C01 Exam Dumps.zip

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

A. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
B. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.
C. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
D. Create a materialized view and schedule a daily refresh at a time of low system activity.
E. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on

Answer: D

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 138
You are working on a data ingestion pipeline that loads data from a CSV file into a Snowflake table called The CSV file occasionally contains invalid characters in the 'Email' column (e.g., spaces, non-ASCII characters). You want to ensure data integrity and prevent the entire load from failing due to these errors. Which of the following strategies, used in conjunction, would BEST handle this situation during the COPY INTO command and maintain data quality?

A. Use a file format with 'VALIDATE UTF8 = TRUE, and 'ON ERROR='SKIP FILE". Create a separate stage containing invalid data to be handled at a later stage with another transformation job
B. Use the 'ON ERROR = 'SKIP FILE" option in the 'COPY INTO' command and then run a subsequent SQL query to identify and correct any invalid email addresses in the 'EmployeeData' table.
C. Use the ERROR = 'SKIP FILE" option in the 'COPY INTO' command along with a file format that specifies 'TRIM SPACE = TRUE and 'ENCODING ='UTF8".
D. Employ the 'VALIDATE function during the 'COPY INTO command to identify erroneous Email columns and use the 'ON ERROR = 'CONTINUE" along with using file format that specifies 'TRIM_SPACE = TRUE and ENCODING = 'UTF8".
E. Use the 'ON ERROR = 'CONTINUE" option in the 'COPY INTO' command. Create a separate error queue table and configure the 'COPY INTO' command to automatically insert error records into the queue.

Answer: D,E

Explanation:
Options B and E provide the most robust solution. ERROR = 'CONTINUE'' allows the load to proceed despite errors. Creating an error queue (implicitly handled by Snowflake if using allows you to examine and address the problematic records later. By including in the file format definition = TRUE' and 'ENCODING = 'UTF8" and 'VALIDATE' function during the 'COPY INTO command to identify erroneous Email columns, you can standardize character encoding. 'SKIP_FILE (options A, C, and D) might lose valuable data. While correcting data with SQL after the load (option C) is possible, capturing the error data directly during the load is more efficient.

NEW QUESTION # 139
You are preparing data for a machine learning model that predicts customer churn. You have a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT'. Your model requires a feature representing the recency of the customer's last transaction (days since last transaction) calculated as of today. Which of the following SQL snippets in Snowflake best achieves this, considering potential performance implications on a large dataset?

Answer: A

Explanation:
The best approach is E because it explicitly casts 'CURRENT _ to a DATE data type using 'CAST(CURRENT This ensures consistency and avoids potential issues if the TRANSACTION DATE' column is also of DATE data type. provides a date only, while provides date and time. Using 'CURRENT directly in 'DATEDIFF with a DATE field could lead to unexpected results due to the time component. Also other options are incorrect because 'GETDATE()' does not exist in snowflake, and because is redundant since CURRENT TIMESTAMP returns a timestamp already

NEW QUESTION # 140
Consider a table 'customer_orderS with columns 'order_id' (INT), 'customer_id' (INT), 'order_date' (DATE), and 'order_total' (NUMBER). The table is partitioned by 'order_date'. You need to create a materialized view that summarizes the total order value per customer, per month. Which of the following materialized view definitions will both achieve the desired summarization and effectively leverage partition pruning for efficient refreshes?

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

Answer: B

Explanation:
Option A is the best choice. It groups by and 'DATE TRUNC('month', order_datey , which creates a monthly aggregation. Since the base table is partitioned by 'order_date' , the materialized view refresh can efficiently use partition pruning based on the monthly date truncations. Option B groups by the full 'order_date' , leading to daily aggregations instead of monthly. Option C uses , which is less efficient and doesn't preserve the year information for partition pruning. Option D uses "CONVERT_TIMEZONE' , which is incorrect for the purpose and will not efficiently leverage partition pruning.Option E using CAST(order_date AS VARCHAR(7)) as it change the date data type.

NEW QUESTION # 141
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 B
C. Option A
D. Option E
E. Option D

Answer: A

Explanation:
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.

NEW QUESTION # 142
......

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SNOWFLAKE DAA-C01 ACTUAL TEST | DAA-C01 EXAM DUMPS.ZIP

Snowflake DAA-C01 Actual Test | DAA-C01 Exam Dumps.zip