Mar-2026 Snowflake DAA-C01 Actual Questions and Braindumps [Q19-Q34]

Mar-2026 Snowflake DAA-C01 Actual Questions and Braindumps

DAA-C01 Dumps To Pass Snowflake Exam in 24 Hours - Dumps4PDF

NEW QUESTION # 19
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

• A. Stored procedures allow limited data accessibility for improved security.
• B. UDFs enhance query performance more effectively than stored procedures.
• C. They enable the execution of repetitive tasks, enhancing efficiency.
• D. Stored procedures hinder customization in data operations.

Answer: C

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.

NEW QUESTION # 20
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

• A. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
• B. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
• C. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
• D. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
• E. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.

Answer: A,C,D,E

Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.

NEW QUESTION # 21
You have a table 'ORDERS that stores customer order information. Due to a faulty ETL process, incorrect discount values were applied to some orders yesterday. You need to correct these discount values while ensuring that you can revert to the original data if necessary. Select all the valid actions to mitigate this situation using Snowflake's Time Travel and Cloning features.

• A. Create a temporary table to store the original incorrect discount values before applying the corrections, then updating the 'ORDERS' table.
• B. Create a clone of the 'ORDERS' table BEFORE applying the corrections, allowing you to revert to the original data by cloning the backup back if needed.
• C. Update the discount values directly in the 'ORDERS' table after verifying the incorrect records, relying solely on Snowflake's data retention for recovery.
• D. Use Time Travel to query the 'ORDERS' table as it existed BEFORE the faulty ETL process, then use this data to correct the discount values in a separate update statement.
• E. Create a backup table with the structure and all the records by using 'create table orders_backup as select from orders'.

Answer: B,D

Explanation:
Options A and E are the most reliable and efficient. Cloning the table (A) before the correction provides a complete, point-in-time backup that can be easily restored. Using Time Travel (E) allows you to query the historical data to identify the correct values and apply targeted updates. B is risky without a backup, as unforeseen issues might prevent successful recovery within the retention period. While C is technically feasible, it is less robust and adds complexity compared to a full clone or Time Travel. D is same as C but with more data, but less robust and adds complexity compared to a full clone or Time Travel. Creating the temp table doesn't give you the benefit of time travel, so doesn't make sense.

NEW QUESTION # 22
When performing forecasting using statistics and built-in functions, what role do these functions play?

• A. They solely aid in data visualization without impacting the forecasting process.
• B. These functions only apply to limited data types for forecasting.
• C. They restrict forecasting to predefined models without flexibility.
• D. They enable the creation of custom forecasting models as needed.

Answer: D

Explanation:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

NEW QUESTION # 23
You are designing a data warehouse for a retail company. The company needs to analyze sales data based on product category, customer demographics, and store location. The sales data is initially stored in a semi-structured JSON format with nested arrays for product details and customer information. The BI team requires optimized query performance for aggregations across these dimensions. Which approach is most suitable for this scenario?

• A. Use a hybrid approach: flatten only the customer demographics into a relational table and keep the product details in a VARIANT column for ad-hoc queries.
• B. Create a single, wide denormalized table containing all sales, product, customer, and store information.
• C. Load the JSON data directly into Snowflake and rely solely on Snowflake's query optimization capabilities without any data modeling.
• D. Create a flattened relational data model with separate tables for sales, products, customers, and store locations, linked using foreign keys.
• E. Load the JSON data directly into a VARIANT column and use lateral views for querying. Avoid any data modeling to minimize initial effort.

Answer: D

Explanation:
Option B is the most suitable approach. A flattened relational data model with separate tables and foreign keys allows for efficient querying and aggregations across different dimensions, which is a key requirement for BI reporting. Flattening the data reduces the overhead of parsing JSON during query execution and enables the use of standard SQL aggregation functions. Option A can lead to performance issues with complex JSON structures. Option D can lead to data redundancy and update anomalies. Option C offers a hybrid approach but can still be inefficient for certain queries. Option E relies too heavily on Snowflake's automatic optimization and will likely underperform compared to a properly designed data model.

NEW QUESTION # 24
How does leveraging partition pruning enhance query performance in Snowflake?

• A. Reduces metadata storage requirements
• B. Limits data access for specific user roles
• C. Optimizes query planning by eliminating unnecessary partitions
• D. Speeds up data loading processes significantly

Answer: C

Explanation:
Partition pruning optimizes query planning by excluding unnecessary partitions from query execution, improving query performance by focusing on relevant data subsets.

NEW QUESTION # 25
How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?

• A. Limits query execution time
• B. Provides real-time data updates
• C. Offers options to alter query execution plans
• D. Enables querying of historical data versions

Answer: D

Explanation:
Time Travel feature allows querying historical data versions, facilitating historical data analysis and retrospective query optimizations based on past data states.

NEW QUESTION # 26
A data analyst is building a dashboard in Tableau connected to Snowflake. The dashboard needs to display the average order value (AOV) for each product category over the last quarter. The Snowflake table 'ORDERS contains columns: 'ORDER ID' , 'ORDER DATE', 'PRODUCT CATEGORY' , and 'ORDER VALUE'. The analyst wants to minimize data transfer from Snowflake to Tableau and optimize dashboard performance. Which of the following approaches is MOST efficient?

• A. Create a Snowflake stored procedure to calculate the AOV and call the stored procedure from Tableau using ODBC connection.
• B. Use Tableau's Custom SQL feature to execute a query that calculates the AOV directly in Snowflake and retrieves only the aggregated results.
• C. Extract all data from the 'ORDERS' table into Tableau and calculate the AOV within Tableau.
• D. Schedule a daily task to export AOV data from Snowflake into a CSV file, and use Tableau to read from the CSV file.
• E. Create a Snowflake view that calculates the AOV and connect Tableau to the view.

Answer: B,E

Explanation:
Both using Tableau's Custom SQL and creating a Snowflake view are efficient options. Custom SQL allows pushing the calculation to Snowflake, minimizing data transfer and leveraging Snowflake's compute power. Creating a view is also a very good approach since it pre-calculates and store the result set as a view. Extracting all data into Tableau is inefficient due to high data transfer. Using a CSV file adds complexity and latency. Stored procedures are typically not designed for direct dashboard interaction; they are better suited for data transformation pipelines.

NEW QUESTION # 27
Which action aids in performing a diagnostic analysis on historical data to identify reasons/causes of anomalies?

• A. Focusing on isolated data points
• B. Analyzing data solely from the past month
• C. Ignoring statistical trends in historical data
• 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 # 28
You are tasked with cleaning a dataset containing customer addresses stored in a column named 'ADDRESS RAW'. The addresses are inconsistent, with varying formats, abbreviations, and missing information. You need to standardize the addresses by extracting key components (street address, city, state, zip code) and storing them in separate columns. Which of the following approaches would be MOST effective for this task, considering the complexity and volume of the data, and the need for maintainability?

• A. Use a series of regular expressions within SQL queries to parse the 'ADDRESS RAW' column and extract the components. Create separate columns for each component and update the table with the extracted values.
• B. Employ Snowflake's built-in string functions (e.g., 'SPLIT, 'TRIM', 'UPPER) in a series of SQL queries to manually parse and standardize the addresses.
• C. Export the data to a data quality tool, perform address standardization and extraction, and then load the cleaned data back into Snowflake.
• D. Create a view with regular expression to parse ADDRESS_RAW.
• E. Develop a UDF (User-Defined Function) in Python that utilizes a dedicated address parsing library (e.g., 'usaddresS) to standardize and extract the address components. Call the UDF in a SQL query to update the table.

Answer: E

Explanation:
Developing a UDF in Python with an address parsing library is the most effective solution. Address parsing libraries are specifically designed to handle the complexities and variations in address formats, providing more accurate and reliable results than manual parsing with regular expressions or string functions. While option A can work for simple cases, it's not scalable or maintainable for complex address formats. Data quality tools (option C) add complexity and cost. Option D is not robust enough for real-world address standardizatiom Option E do not store data on a column.

NEW QUESTION # 29
A data analyst is implementing a data preparation pipeline using Snowflake stored procedures to cleanse and transform data,. During testing, the analyst encounters unexpected errors within the stored procedures. Which strategies should the analyst employ to effectively debug and troubleshoot these stored procedures within Snowflake?

• A. Rely solely on the error messages returned by Snowflake when the stored procedure fails.
• B. Debug by trial and error, modifying the stored procedure code and re-executing until the error is resolved.
• C. Implement a custom error logging mechanism within the stored procedure using 'SYSTEM$LOG' to capture error messages and write them to a dedicated logging table.
• D.
• E. Use the 'GET function on the stored procedure to review the code for potential errors.

Answer: C,D

Explanation:
Option A describes the correct method for obtaining the query ID of the failing procedure and querying the query history for detailed error information. Option B is also correct, implementing detailed error handling within the stored procedure provides greater insight into where the procedure fails. Option C is insufficient, Snowflake's general error messages are not always descriptive. Option D does not help during debugging and troubleshooting a running stored procedure. Option E is an inefficient and unreliable debugging approach.

NEW QUESTION # 30
You are building a data pipeline to ingest customer data into Snowflake. You have identified a need to dynamically determine the data load timestamp during the ingestion process itself, without relying on external systems or pre-defined variables. Which system function(s) would be the MOST appropriate and efficient choice to accomplish this?

• A.
• B. NOW()
• C. CURRENT _ TIMESTAMP()
• D. GETDATE()
• E. SYSDATE()

Answer: C

Explanation:
The function returns the current timestamp at the start of the statement. 'SYSDATE()' and 'GETDATE()' functions does not exists in Snowflake. is a synonym for CURRENT TIMESTAMP(). However, is not a standard documented function.

NEW QUESTION # 31
A Data Analyst needs to temporarily hide a tile in a dashboard. The data will need to be available in the future, and additional data may be added. Which tile should be used?

• A. Unplace
• B. Duplicate
• C. Delete
• D. Show/Hide

Answer: A

Explanation:
In Snowsight, managing dashboard layouts requires an understanding of how tiles (queries or visualizations) are stored versus how they are displayed. When an analyst wants to remove a tile from the visible dashboard grid without destroying the underlying query logic or historical configuration, the Unplace action is the correct functional choice.
When a tile is unplaced, it is removed from the dashboard's active layout but remains part of the dashboard's
"library" of available content. This is a critical distinction from the Delete action (Option C), which permanently removes the tile and its associated SQL code from the dashboard object. Unplacing allows the analyst to "archive" the work temporarily. Because the tile still technically exists within the dashboard's metadata, any new data added to the underlying tables will still be processed by the query whenever the tile is eventually placed back onto the grid.
Evaluating the Options:
* Option A (Show/Hide) is not a standard standalone command for dashboard tile management in Snowsight; visibility is typically managed through placement on the grid.
* Option B (Duplicate) creates a second copy of the tile. While this preserves the data, it does not satisfy the requirement to "hide" the current tile; it actually adds more clutter to the dashboard.
* Option C (Delete) is incorrect because the prompt specifies that the data and tile will need to be available in the future. Deleting would require the analyst to rewrite the SQL and reconfigure the visualization from scratch.
* Option D is the 100% correct answer. Unplacing is the "soft-remove" feature of Snowsight. It preserves the tile in the "Unplaced Tiles" sidebar, allowing for quick restoration at a later date. This feature is essential for analysts who need to manage evolving reporting requirements where certain metrics may only be relevant seasonally or during specific business cycles.

NEW QUESTION # 32
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?

• A.
• B.
• C.
• D.
• E.

Answer: A

Explanation:
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.

NEW QUESTION # 33
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

• A. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
• B. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
• C. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
• D. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
• E. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.

Answer: B,E

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 34
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