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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

A. Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
B. Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.
C. Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.
D. Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.
E. Enforce rules using stored procedure at the time of insertion and updation.

Answer: B

Explanation:
Option D is the most appropriate and efficient. CHECK constraints are designed for these types of validations. The 'CASE' statement within the third CHECK constraint allows conditional validation based on the 'DEPARTMENT value. CHECK constriants are enforced at the time of record insert or update. Stored procedures could be an option but are not the most appropriate. Snowflake does not directly support ENUM types for column definitions. Creating Lookup table with Foreign key is another option.

NEW QUESTION # 134
What attributes of the Query Profile are typically assessed to understand query performance in Snowflake?

A. Query history and user access logs
B. Number of user sessions and database objects accessed
C. Execution time and query complexity
D. Hardware configuration and system resources

Answer: C

Explanation:
Query Profile attributes like execution time and query complexity are evaluated to comprehend query performance and optimize it in Snowflake.

NEW QUESTION # 135
Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply

Answer: C,D

Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR

NEW QUESTION # 136
You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?

A. Employ a third-party ETL tool integrated with Snowflake, configure data cleansing and transformation rules within the ETL tool, and load the transformed data into Snowflake using its optimized connectors.
B. Utilize Dynamic Tables with appropriate transformation logic defined to handle inconsistencies and maintain data quality during ingestion. Leverage Snowflake's data governance features in conjunction with Dynamic Tables for comprehensive data management
C. Leverage Snowflake's data governance features with data profiling, implement data quality rules in a data catalog, and use a combination of Snowpipe and COPY INTO with data transformations within the COPY INTO statement.
D. Use Snowpipe with schema evolution enabled and rely solely on Snowflake's automatic data type conversion. Implement data quality checks in downstream views.
E. Build a custom data ingestion application using the Snowflake Connector for Python, implementing complex data transformations and quality checks within the application before loading the data into Snowflake. Store raw data as is for future use.

Answer: A,B,C,E

Explanation:
Options B, C, D, and E represent robust approaches. B combines data governance and built-in transformations. C allows for custom logic using the Python connector. D uses a specialized ETL tool. E leverages Dynamic Tables for automated transformations and data quality maintenance. A is insufficient as it relies too heavily on automatic conversion and delays quality checks. The best approach would involve data profiling to understand the data, implementing data quality rules at the point of ingestion or through dynamic tables, and leveraging a transformation approach suited to the complexity of the transformations.

NEW QUESTION # 137
Consider a scenario where you're analyzing website user behavior data in Snowflake. You have a table named 'user sessionS with a column containing semi-structured data (VARIANT type) describing user interactions during a session. You need to create a UDF that accepts and extracts all the distinct event types that occurred within that session. The UDF should return an array of unique event type strings. This array will be used later to identify users who have participated in a specific combination of events. Which of the following approaches can effectively achieve this using Snowflake's SQL extensibility features?

A. All of the above can satisfy the question. (Single Correct Answer)
B. Implementing a Java UDF that parses the session data, extracts the event types, uses Java's HashSet to guarantee uniqueness, and returns the HashSet as a comma-separated string. (Single Correct Answer)
C. Using a JavaScript UDF that iterates through the session data, extracts the event types, adds them to a JavaScript Set to ensure uniqueness, and then converts the Set to an array. This array is returned as a result. (Single Correct Answer)
D. Creating a SQL UDF that utilizes Snowflake's ARRAY AGG and DISTINCT functions to aggregate all event types from the session data into an array, ensuring uniqueness. (Single Correct Answer)
E. Using an external function with a remote service to process 'session_data' and return JSON array with distinct event types. The external function setup must include API integration with the third party service and Role based access control. (Single Correct Answer)

Answer: C

Explanation:
The most efficient and appropriate solution is A. A JavaScript UDF allows for direct manipulation of the session data (VARIANT) and leveraging JavaScript's Set object for efficient uniqueness enforcement before returning the result as an array. This minimizes data transformation overhead within Snowflake's SQL engine. B: While SQL UDFs are an option, processing nested data and enforcing uniqueness within SQL can be less efficient compared to JavaScript's built-in capabilities. C: Using java, while possible, add overhead to the processing since Java UDF requires to setup class definitions and imports which can be an overkill for this use case. D: This is an external API integration which has extra overhead and latency. E: Is incorrect because all approaches have tradeoffs and can be implemented in certain instances based on requirements.

NEW QUESTION # 138
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Free Sample DAA-C01 Questions - Study DAA-C01 Center

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