DAA-C01 REAL DUMPS FREE & DAA-C01 TEST OBJECTIVES PDF

DAA-C01 Real Dumps Free & DAA-C01 Test Objectives Pdf

DAA-C01 Real Dumps Free & DAA-C01 Test Objectives Pdf

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100% Pass Quiz Snowflake DAA-C01 - SnowPro Advanced: Data Analyst Certification Exam Marvelous Real Dumps Free

At the TestsDumps, we strive to provide our customers with updated and real Snowflake DAA-C01 exam questions. We are committed to helping our students reach their goals and advance their careers through comprehensive, convenient, and cost-effective Prepare for your SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) exam preparation material.

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q173-Q178):

NEW QUESTION # 173
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?

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

Answer: B

Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.


NEW QUESTION # 174
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?

  • A. Use the query history to determine the average execution time for similar queries and choose a warehouse size that is slightly larger than the one used for those queries.
  • B. Start with the smallest warehouse size and incrementally increase the size, monitoring query execution time and cloud services usage. Stop increasing the size when query time plateaus or cloud services usage increases significantly.
  • C. Increase the warehouse size to the largest available size and monitor query execution time and cloud services usage. If cloud services usage is high, decrease the warehouse size until a balance is achieved.
  • D. Set the parameter to a low value to prevent long-running queries and force Snowflake to automatically optimize the warehouse size.
  • E. Run the query multiple times with different warehouse sizes, recording the execution time for each size. Choose the warehouse size with the lowest execution time, regardless of cloud services usage.

Answer: B

Explanation:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.


NEW QUESTION # 175
A Snowflake data warehouse contains a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID, 'TRANSACTION DATE', 'AMOUNT', and 'PRODUCT CATEGORY'. Analysts frequently run queries that aggregate transaction amounts by product category for specific customer segments. The following query pattern is common:

Which of the following strategies, when implemented together, would BEST optimize the performance of this query pattern, considering both result caching and data access patterns?

  • A. Cluster the 'CUSTOMER TRANSACTIONS' table by 'CUSTOMER and then 'TRANSACTION DATE. Create a materialized view preaggregating 'SUM(AMOUNT)' by PRODUCT_CATEGORY', and ' TRANSACTION_DATE
  • B. Tune the virtual warehouse size to be as small as possible while still meeting performance requirements. Ensure the statistics on the table are up to date.
  • C. create a materialized view that pre-aggregates 'SUM(AMOUNT)' by 'PRODUCT_CATEGORY, 'CUSTOMER_ID , and 'TRANSACTION_DATE. Regularly refresh the materialized view.
  • D. Create indexes on 'CUSTOMER and 'TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.
  • E. Create a view that encapsulates the 'WHERE' clause conditions (filtering by 'CUSTOMER ID and 'TRANSACTION DATE). Enable automatic query rewrite.

Answer: A

Explanation:
Clustering 'CUSTOMER TRANSACTIONS' by 'CUSTOMER and 'TRANSACTION DATE improves data access performance by organizing data physically based on the frequently used filter criteria. Creating a materialized view that pre-aggregates the results by PRODUCT CATEGORY, 'CUSTOMER ID', and 'TRANSACTION DATE' allows Snowflake to serve the results directly from the materialized view, significantly reducing the compute cost and improving query performance. Snowflake does not use traditional indexes; clustering provides similar benefits.


NEW QUESTION # 176
You are analyzing website traffic data in Snowflake. The 'web_events' table contains 'event_timestamp' (TIMESTAMP N T Z), 'user_id', and 'page_url'. You discover that many 'event_timestamp' values are significantly skewed towards the future (e.g., a year ahead), likely due to incorrect device clocks. You want to correct these skewed timestamps by assuming the majority of events are valid and calculating a time drift. Which of the following strategies using Snowflake functionality would be MOST efficient and accurate for correcting these timestamps?

  • A. Calculate the median 'event_timestamp' for each 'user_id' and subtract the overall median 'event_timestamp' from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
  • B. Calculate the average 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the average. Subtract this difference from the future skewed events to correct them.
  • C. Calculate the average 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
  • D. Calculate the median 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the median. Subtract this difference from the future skewed events to correct them.
  • E. Calculate the mode of the 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.

Answer: D

Explanation:
Option D provides the most robust approach. Using the median minimizes the impact of outliers (future-dated timestamps). Calculating the difference between each event timestamp and the overall median timestamp isolates the 'time_drift' for each record, which is then subtracted from each future skewed events. Option A uses median for each user, which is unneccesary. Options B and E are vulnerable to outliers (the very problem we're trying to solve). Option C, while conceptually interesting, isn't directly supported as a native aggregate function for timestamps in most SQL dialects, including Snowflake, without custom user-defined functions (UDFs), making it less efficient and potentially less accurate.


NEW QUESTION # 177
You have a table 'ORDERS that stores customer order information. Due to a faulty ETL process, incorrect discount values were applied to some orders yesterday. You need to correct these discount values while ensuring that you can revert to the original data if necessary. Select all the valid actions to mitigate this situation using Snowflake's Time Travel and Cloning features.

  • A. Update the discount values directly in the 'ORDERS' table after verifying the incorrect records, relying solely on Snowflake's data retention for recovery.
  • B. Create a temporary table to store the original incorrect discount values before applying the corrections, then updating the 'ORDERS' table.
  • C. Use Time Travel to query the 'ORDERS' table as it existed BEFORE the faulty ETL process, then use this data to correct the discount values in a separate update statement.
  • D. Create a backup table with the structure and all the records by using 'create table orders_backup as select from orders'.
  • E. Create a clone of the 'ORDERS' table BEFORE applying the corrections, allowing you to revert to the original data by cloning the backup back if needed.

Answer: C,E

Explanation:
Options A and E are the most reliable and efficient. Cloning the table (A) before the correction provides a complete, point-in-time backup that can be easily restored. Using Time Travel (E) allows you to query the historical data to identify the correct values and apply targeted updates. B is risky without a backup, as unforeseen issues might prevent successful recovery within the retention period. While C is technically feasible, it is less robust and adds complexity compared to a full clone or Time Travel. D is same as C but with more data, but less robust and adds complexity compared to a full clone or Time Travel. Creating the temp table doesn't give you the benefit of time travel, so doesn't make sense.


NEW QUESTION # 178
......

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