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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
A financial institution uses Snowflake to store transaction data'. They observe an unexpected spike in fraudulent transactions during a specific weekend. The 'TRANSACTIONS table contains columns like 'transaction id', 'transaction date', 'amount', 'merchant_category', 'user_id', and 'is_fraudulent. Which of the following approaches, leveraging Snowflake's capabilities, would be MOST effective in diagnosing the potential causes of this fraud spike?

A. Join the 'TRANSACTIONS table with a table containing external threat intelligence data (e.g., IP address reputation, known fraudulent merchants) to identify transactions originating from suspicious sources.
B. Use a machine learning model (deployed as a Snowflake user-defined function) to re-score all transactions from the affected weekend based on historical patterns and identify potentially misclassified transactions.
C. Create cohorts of users based on transaction behavior before the spike and compare their transaction patterns during the spike to identify anomalies within specific user groups. Use window functions like 'FIRST_VALUE to define cohorts.
D. Only check the transaction details, like amount and the users involved, and assume one of the users triggered those Fraud Trasanctions.
E. Analyze transaction patterns by 'merchant_category' and 'user_id' before, during, and after the spike to identify any unusual concentrations or emerging fraudulent schemes. Consider using 'QUALIFY' clause to filter top N merchant_category by transaction amount.

Answer: A,B,C,E

Explanation:
Options A, B, C, and D provide a robust diagnostic approach. A uses machine learning for anomaly detection, B analyzes patterns by merchant category and user, C incorporates external threat intelligence, and D examines cohort behavior. Only checking specific transaction is not effective.

NEW QUESTION # 156
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

A. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
B. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
C. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
D. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.
E. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.

Answer: B

Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

NEW QUESTION # 157
You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?

Answer: D

Explanation:
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.

NEW QUESTION # 158
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)

A. Secure Views can be used in conjunction with Row Access Policies to control data visibility at a granular level. Materialized views cannot be used in conjunction with row access policies.
B. Materialized Views can only be created on tables, not on other views or functions.
C. Secure Views incur a performance overhead compared to standard views, while Materialized Views generally improve query performance for suitable workloads.
D. Both Secure Views and Materialized Views can be automatically refreshed based on data changes in the underlying tables.
E. Secure Views obfuscate the underlying query logic from users, while Materialized Views store pre-computed data for faster query performance.

Answer: C,E

Explanation:
Secure Views hide query logic and data lineage, improving security. Materialized Views pre-compute and store data, improving performance. Secure Views can have a performance overhead as additional checks are performed. Materialized Views are automatically refreshed in Snowflake. Materialized views cannot be created with User-Defined Functions in the Select List or Where Clause. Row access policies are not supported on materialized views.

NEW QUESTION # 159
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

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

Answer: B

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.

NEW QUESTION # 160
......

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EXAM SNOWFLAKE DAA-C01 REVIEWS | DAA-C01 VALID EXAM BOOTCAMP

Exam Snowflake DAA-C01 Reviews | DAA-C01 Valid Exam Bootcamp