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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?

A. They limit data representation options for simplicity.
B. Charts don't impact data visualization in reports or dashboards.
C. Different chart types offer varied data representation for better analysis.
D. Diverse chart types restrict data exploration in reports and dashboards.

Answer: C

Explanation:
Different chart types offer varied data representation, aiding better analysis in reports and dashboards.

NEW QUESTION # 20
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 constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.
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 constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
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 # 21
You are tasked with ingesting data from a REST API that provides daily sales reports in JSON format. The API has rate limits (100 requests per minute) and returns a large dataset (approximately 5GB per day). The data needs to be processed within 2 hours of its availability. You want to leverage Snowflake external functions and tasks. Which approach balances efficiency, cost, and adherence to rate limits?

A. Call API directly from a scheduled task without considering Rate Limit. Persist the data using COPY INTO command.
B. Create a Snowflake task that calls an external function. This external function calls an intermediate service (e.g., AWS Lambda, Azure Function) which is responsible for fetching the data in batches, respecting the API rate limits, and storing the data in cloud storage. Snowpipe then loads the data from cloud storage into Snowflake.
C. Create a Snowflake external function that directly connects to the API and loads data into a staging table. Use Snowpipe with auto-ingest to continuously load the data as it arrives. Ignore the API Rate limits; assume they will be handled by API itself.
D. Create a Snowflake task that triggers an external function which retrieves only the metadata (e.g., total records, page count) from the API. Then, create a dynamic number of child tasks, each responsible for retrieving a subset of the data, using the metadata to respect the API rate limits.
E. Create a single Snowflake task that calls an external function which iterates through all API pages sequentially in a single execution to retrieve and load all the data. Rely on Snowflake's automatic scaling to handle the load.

Answer: B

Explanation:
Option D is the most balanced approach. It leverages an intermediate service to handle the complexities of API interaction (rate limits, pagination), decouples the data retrieval from Snowflake compute, and uses Snowpipe for efficient bulk loading. This approach addresses I both the rate limits and processing time requirements effectively.

NEW QUESTION # 22
What effect do row access policies have on the creation of dashboards concerning user data visibility?

A. Row access policies offer unrestricted data visibility.
B. Row access policies don't influence data visibility in dashboards.
C. They limit data visibility based on user privileges.
D. Row access policies impact dashboard creation negatively.

Answer: C

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.

NEW QUESTION # 23
You are designing a data pipeline to ingest JSON data from an external stage (AWS S3) into a Snowflake table called 'ORDERS' Some of the JSON files contain nested arrays that need to be flattened and transformed during the loading process. You have already defined a VARIANT column in the 'ORDERS table to store the raw JSON data'. However, occasionally, some files fail to load completely, and the 'SYSTEM$PIPE STATUS' shows a 'LOAD FAILED' status without providing granular details about the specific records causing the failure. Which of the following strategies, used IN COMBINATION, would be MOST effective in troubleshooting and resolving these failures while minimizing the impact on the overall data ingestion process?

A. Enable Snowpipe's 'ERROR_INTEGRATION' , examine the error logs for malformed JSON records, and adjust the COPY INTO statement with appropriate FILE_FORMAT options to handle the nested arrays.
B. Increase the compute resources allocated to the virtual warehouse used by the Snowpipe. Also, disable Snowpipe and load the data manually using the COPY INTO command to identify any errors during load.
C. Enabling the ERROR=CONTINUE parameter on the COPY INTO statement used in the pipe, and regularly querying the function to identify issues in the loaded data.
D. Use a 'VALIDATE' statement with the same COPY INTO statement to identify records that will fail. Then, modify the COPY INTO statement to handle the errors or exclude the problematic records using a 'WHERE clause in the transformation logic of the COPY INTO statement or inline SQL Transformations
E. Implement a pre-processing step using a Snowflake task to validate the JSON data before loading it into the 'ORDERS' table using a COPY INTO statement. The Task would filter out bad records.

Answer: A,D

Explanation:
ERROR INTEGRATION' allows you to inspect individual error records and identify patterns in those failing files. The 'VALIDATE' function allows you to perform a COPY INTO using similar parameters as your copy into statement to validate the record, and helps you tune your data pipeline for errors. Option B is viable, but has increased maintenance overhead compared to VALIDATE, because you would need to write code for the preprocessing. Option D focuses on resource allocation, which doesn't directly address data quality issues. Option E by itself only attempts to continue, and doesn't do any validation. 'ON is a good idea when paired with validating the data after the load.

NEW QUESTION # 24
......

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Snowflake DAA-C01 Reasonable Exam Price | DAA-C01 Latest Exam Tips

Quiz 2025 Snowflake Latest DAA-C01 Reasonable Exam Price

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q19-Q24):

Snowflake DAA-C01 Reasonable Exam Price Are Leading Materials with High Pass Rate

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