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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
A financial institution needs to categorize transactions as 'High Risk', 'Medium Risk', or 'Low Risk' based on a complex set of rules involving transaction amount, location, merchant type, and customer history. You are using Snowflake to implement this classification. Which of the following approaches would provide the MOST flexible and scalable solution for defining and managing these risk classification rules, and then applying them to incoming transaction data?

A. Leveraging Snowflake's external functions to call a rules engine deployed on a separate server (e.g., a Drools rules engine running on AWS EC2) to perform the risk classification.
B. Using Snowflake's Python User-Defined Functions (UDFs) to encapsulate the risk classification logic, allowing for more complex calculations and the use of external libraries if needed.
C. Creating a series of Snowflake Tasks that execute SQL queries to categorize transactions based on individual rules, chaining these tasks together in a specific sequence.
D. Implementing the classification logic directly within a single, large SQL query using nested 'CASE' statements and complex conditional logic.
E. Storing the risk classification rules in a separate Snowflake table and using a dynamic SQL query to generate and execute the classification logic based on the data in the rules table.

Answer: A,B

Explanation:
Options C and E are the most flexible and scalable. Option C enables the creation of complex logic with Snowflake UDF, while option E uses external functions for connecting to separate rule engines. Option A would become unwieldy and difficult to maintain as the number of rules increases. Option B may not be suitable for real-time classification and may introduce unnecessary complexity. Option D, while offering some flexibility, can be challenging to implement and optimize for complex rules.

NEW QUESTION # 65
Which action aids in performing a diagnostic analysis on historical data to identify reasons/causes of anomalies?

A. Focusing on isolated data points
B. Ignoring statistical trends in historical data
C. Analyzing data solely from the past month
D. Collecting related data and demographics

Answer: D

Explanation:
Collecting related data and demographics is crucial in understanding the reasons/causes of anomalies in historical data.

NEW QUESTION # 66
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?

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

Answer: B,D

Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.

NEW QUESTION # 67
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

A. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
B. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
C. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
D. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
E. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;

Answer: E

Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.

NEW QUESTION # 68
Which considerations are part of best practice for ensuring data integrity structures in Snowflake?
(Select all that apply)

A. Implementing redundant constraints
B. Using primary keys for tables
C. Ensuring data normalization
D. Establishing parent-child table joins

Answer: B,D

Explanation:
Data integrity practices in Snowflake involve using primary keys for tables and establishing effective parent-child table joins.

NEW QUESTION # 69
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New DAA-C01 Test Syllabus & Exam DAA-C01 Pattern

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New DAA-C01 Test Syllabus High-quality Questions Pool Only at Actual4Dumps

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q64-Q69):

Well-Prepared New DAA-C01 Test Syllabus & Leading Provider in Qualification Exams & Free PDF Exam DAA-C01 Pattern

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