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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are analyzing customer churn for a subscription-based service. You have a table 'SUBSCRIPTIONS' with columns: 'CUSTOMER_ID, 'START_DATE', 'END_DATE', 'SUBSCRIPTION TYPE, and 'REVENUE'. You want to classify customers who are likely to churn based on their past subscription behavior. Which Snowflake SQL code snippet is MOST efficient for calculating the number of months each customer was subscribed and identifying those who subscribed for less than 3 months as potential churn candidates?

Answer: E

Explanation:
Option D is the most efficient. It calculates the 'MONTHS SUBSCRIBED directly in the SELECT statement and filters the results in the 'WHERE clause. Option A will produce an error because you cannot refer to an alias (MONTHS_SUBSCRIBED) in the same 'WHERE clause where it's defined. Option B creates a temporary table, which is unnecessary overhead for this simple calculation. Option C uses HAVING& which is used for filtering aggregated results, not individual rows before aggregation. Snowflake does not natively support MONTHS_BETWEEN, thus option E would result in an error.

NEW QUESTION # 72
What is the primary purpose of implementing data processing solutions?

A. Automate data analysis entirely
B. Simplify data access for users
C. Respond effectively to processing failures
D. Limit data processing to batch operations

Answer: C

Explanation:
Data processing solutions help respond effectively to processing failures for uninterrupted operations.

NEW QUESTION # 73
Why are materialized views valuable in data analysis compared to regular views?

A. Materialized views provide precomputed snapshots, improving query performance.
B. Materialized views restrict data access, enhancing security.
C. Regular views simplify complex data structures for ease of analysis.
D. They offer real-time updates reflecting instantaneous database changes.

Answer: A

Explanation:
Materialized views provide precomputed snapshots, improving query performance.

NEW QUESTION # 74
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. 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".
B. 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
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. 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.
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: A,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 # 75
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?

Answer: C

Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.

NEW QUESTION # 76
......

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New DAA-C01 Real Test | DAA-C01 Latest Exam Pattern

DAA-C01 Latest Exam Pattern, New Study DAA-C01 Questions

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q71-Q76):

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