[Mar-2026] Pass Snowflake DAA-C01 Exam in First Attempt Guaranteed!
Full DAA-C01 Practice Test and 67 unique questions with explanations waiting just for you, get it now!
NEW QUESTION # 27
When performing forecasting, which factors are essential for accurate predictions? (Select all that apply)
- A. Relying solely on historical data without considering external influences
- B. Examining trends and anomalies in historical data
- C. Using basic arithmetic functions exclusively for forecasting
- D. Incorporating statistical methods for prediction accuracy
Answer: B,D
Explanation:
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.
NEW QUESTION # 28
A company is looking for new headquarters and wants to minimize the distances employees have to commute.
The company has geographic data on employees' residences. Through the Snowflake Marketplace, the company obtained geographic data for possible locations of the new headquarters. How can the distance between an employee's residence and potential headquarters locations be calculated in meters with the LEAST operational overhead?
- A. ST_DISTANCE
- B. HAVERSINE
- C. ST_HAUSDORFFDISTANCE
- D. ST_LENGTH
Answer: A
Explanation:
Snowflake provides native support for geospatial analysis through the GEOGRAPHY and GEOMETRY data types, along with a suite of Standardized Spatial Functions. To calculate the "least distance" between two geographic points (such as an employee's home and a potential office site) on the Earth's surface, the most efficient and direct function is ST_DISTANCE.
The ST_DISTANCE function takes two GEOGRAPHY objects as input and returns the minimum geodesic distance between them. A key benefit of using this native function for Data Analysis is that it automatically returns the result in meters by default when used with the GEOGRAPHY type, which models the Earth as a spheroid. This eliminates the need for manual mathematical conversions or complex custom logic, satisfying the "least operational overhead" requirement.
Evaluating the Options:
* Option A (ST_HAUSDORFFDISTANCE) is used to measure the similarity between two shapes (geometries), not the simple distance between two points.
* Option B (HAVERSINE) is a mathematical formula that can be implemented manually in SQL, but it requires significantly more code and "operational overhead" compared to a single built-in function.
* Option C (ST_LENGTH) is used to measure the total length of a LineString or the perimeter of a Polygon, rather than the distance between two distinct objects.
* Option D is the 100% correct answer. It is the optimized, native Snowflake function for point-to-point distance calculations in the Data Cloud.
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NEW QUESTION # 29
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?
- A. Maintain the original normalized tables ('PRODUCTS', 'SALES', 'CUSTOMERS') and create views that join these tables as needed for reporting purposes.
- B. Design a fact table CSALES_FACT) with foreign keys to dimension tables for 'PRODUCTS', 'CUSTOMERS, and a DATE' dimension. The 'SALES_FACT table should contain measures such as 'SALES AMOUNT and 'QUANTITY SOLD.
- C. Create a single, denormalized table containing all product, sales, and customer information to maximize query performance.
- D. Use a snowflake schema design, where the dimension tables (e.g., 'PRODUCTS', 'CUSTOMERS) are further normalized into related tables to reduce data redundancy.
- E. create separate fact tables for each product category (e.g., and link them to the 'CUSTOMERS' dimension table.
Answer: B,D
Explanation:
Option B accurately describes a star schema design with a central fact table (SALES FACT) and dimension tables for products and customers. The fact table contains the numerical measures, and the dimension tables provide the context for analysis. Option D correctly describes Snowflake design, which is an extension of Star schema. Option A is generally not recommended because it leads to data redundancy and potential inconsistencies. Option C is not scalable or maintainable as the product range expands. Option E would result in slower query performance compared to a star schema design.
NEW QUESTION # 30
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. Use the ERROR = 'SKIP FILE" option in the 'COPY INTO' command along with a 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 '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.
- D. 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.
- E. 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".
Answer: D,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 # 31
You have a Snowpipe configured to load CSV files from an AWS S3 bucket into a Snowflake table. The CSV files are compressed using GZIP. You've noticed that Snowpipe is occasionally failing with the error 'Incorrect number of columns in file'. This issue is intermittent and affects different files. Your team has confirmed that the source data schema should be consistent. What combination of actions provides the most likely and efficient solution to address this intermittent column count mismatch issue?
- A. Adjust the parameter in the file format to FALSE. This will allow Snowpipe to load the data, skipping rows with incorrect column counts. Implement a separate process to identify and handle skipped rows.
- B. Set the 'SKIP_HEADER parameter in the file format to 1 and ensure that a header row is consistently present in all CSV files. Also implement a task that validates that the header of all CSV files are correct.
- C. Investigate the compression level of the GZIP files. Some compression levels might lead to data corruption during decompression, causing incorrect column counts. Lowering the compression might help.
- D. Recreate the Snowflake table with a 'VARIANT column to store the entire CSV row as a single field. Then, use SQL to parse the 'VARIANT* data into the desired columns.
- E. Check for carriage return characters within the CSV data fields. These characters can be misinterpreted as row delimiters, leading to incorrect column counts. Use the and 'RECORD_DELIMITER parameters in the file format to correctly parse the CSV data.
Answer: A,E
Explanation:
Setting *ERROR ON COLUMN COUNT MISMATCH' to FALSE allows the pipe to continue without halting on such errors. However, this approach will leave behind bad records. Carriage return issues can occur, which affect the column count when ingesting data. If there are carriage return characters inside the CSV fields, this will be misinterpreted as delimiters. Option A might help if headers are present and consistent, but is less likely the root cause of an intermittent column count mismatch. Option C is unlikely to be a primary cause of column count issues as GZIP decompression is generally reliable. Option E is a workaround, but less efficient than correctly configuring the CSV parsing.
NEW QUESTION # 32
Which aspects are important when making predictions based on data for forecasting purposes?
(Select all that apply)
- A. Using only basic arithmetic functions for forecasting
- B. Incorporating statistical methods for accurate predictions
- C. Considering trends and anomalies in historical data
- D. Relying solely on historical data without considering external factors
Answer: B,C
Explanation:
Incorporating statistical methods and considering trends and anomalies are crucial for accurate predictions in forecasting.
NEW QUESTION # 33
Which of the following is a key step in data preparation?
- A. Algorithm selection
- B. Model deployment
- C. Visual analysis
- D. Data normalization
Answer: D
NEW QUESTION # 34
In Snowflake, what factors determine the effectiveness of using materialized views for query optimization?
- A. Compatibility with specific BI tools only
- B. Limitations in accessing historical data
- C. Frequency of data updates and refresh requirements
- D. Query result caching capabilities
Answer: C,D
Explanation:
Materialized views' effectiveness depends on factors like data update frequency and query result caching, impacting query optimization based on the nature of data updates and caching capabilities.
NEW QUESTION # 35
A financial institution needs to build a dashboard to monitor fraudulent transactions. They have transaction data (amount, timestamp, location, merchant category), customer data (age, income, credit score), and fraud flags. They want to identify patterns and correlations that indicate potentially fraudulent activity. Which of the following visualizations and Snowflake features, when used in combination, would be MOST effective for this purpose? (Select TWO)
- A. Use Snowflake's secure data sharing to share the raw transaction data with a third-party analytics vendor.
- B. Create a static report showing the total number of fraudulent transactions per month, using Snowflake's aggregate functions.
- C. Develop an interactive dashboard using Power BI connected to Snowflake, incorporating a geographical map showing transaction locations, a time series chart of transaction volume, and a scatter plot of transaction amount vs. customer credit score. Use anomaly detection algorithms in Snowflake (via Snowpark or stored procedures using Python) to highlight unusual transactions. Implement User defined table Function in Power BI to generate more data.
- D. Export the transaction data to a data lake and use Spark to perform fraud detection.
- E. Utilize a heatmap visualization in a Tableau dashboard to show the correlation between merchant category, transaction amount, and fraud flag. Implement interactive filters for customer age, income, and location. Implement Snowflake Row Access Policy to restrict data to specific users.
Answer: C,E
Explanation:
Options B and D are the most effective. Option B utilizes an interactive dashboard with Power BI, incorporating geographical, temporal, and scatter plot visualizations to identify patterns. Anomaly detection algorithms in Snowflake further enhance the detection capabilities. Option D uses a heatmap to show correlations between key variables and implements interactive filters for detailed exploration. Implementing Row Access Policy would control data access, adding a security layer on top of sensitive financial data. Option A is not related to visualization or identifying correlations. Option C provides only a high-level summary. Option E involves exporting data outside Snowflake, which is less efficient and potentially less secure than using Snowflake's built-in capabilities.
NEW QUESTION # 36
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?
- A. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
- B. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
- C. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.
- D. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
- E. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.
Answer: B
Explanation:
Parameterized views allow you to create flexible queries that adapt to user selections. Clustering keys ensure efficient filtering and data retrieval for drill-down operations. Creating multiple dashboards (B) is less efficient and user-friendly. Relying solely on dashboard filtering (C) can lead to performance issues. Exporting data to an external BI tool (D) introduces latency. Dynamic SQL generation (E) can be complex and prone to errors.
NEW QUESTION # 37
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?
- A. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.
- B. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
- C. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.
- D. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
- E. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
Answer: E
Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.
NEW QUESTION # 38
You need to create an exact, point-in-time copy of a production database named 'CUSTOMER DATA for testing purposes, without impacting the performance of the production system. You want to ensure that the test database 'CUSTOMER DATA TEST uses minimal storage. Which of the following Snowflake commands provides the MOST efficient way to achieve this?
- A.
- B.
- C.
- D.
- E.
Answer: D
Explanation:
The 'CREATE DATABASE CUSTOMER DATA TEST CLONE CUSTOMER DATA;' command is the MOST efficient because it leverages Snowflake's zero-copy cloning feature. Cloning creates a metadata copy of the database; data is only physically copied when changes are made to the clone. This minimizes storage usage and has a minimal performance impact on the production database. Option A is used for replication across accounts/regions. Option B is not a valid Snowflake command. Option D only copies the structure, and not the data. Option E copies the data into a table within a new or existing database, not cloning the entire database structure.
NEW QUESTION # 39
You have a Snowflake table called 'CUSTOMER ORDERS that stores customer order data'. The business requires you to generate a weekly report on the top 10 customers by order value, delivered as an Excel file to a shared network drive. The network drive is accessible by a service account that your Snowflake account can authenticate against. The report must include customer name, total order value, and number of orders. Which approach is the MOST secure and efficient for automating this process?
- A. Leverage a Snowflake Task to run a stored procedure. The procedure queries the data, transforms it into CSV format using Snowflake scripting. Then uses a Java UDF to copy the CSV to an internal stage, from where a separate process (outside Snowflake) monitors for new files and transfers them to the network drive using the service account. Securely manage credentials for both the Java UDF and the external process.
- B. Use a Snowflake Task to trigger a Snowpipe. A Snowflake stored procedure that executes SQL code to query for relevant data, convert it to JSON, then the Snowpipe load into the network directory using REST API. Grant necessary permissions to the task's service account.
- C. Create a view on top of the CUSTOMER_ORDERS table that calculates the required metrics. Use a third-party ETL tool to extract the data from the view, format it as an Excel file, and save it to the network drive. Configure the ETL tool with appropriate Snowflake credentials.
- D. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format the data using Javascript UDF to XML, write the Excel file to an internal stage using Java UDF, and then use a Python UDF to copy the file to the network drive. Grant necessary permissions to the task's service account.
- E. Create a Snowflake external function using AWS API Gateway and AWS Lambda. The external function queries the data from Snowflake, formats it as an Excel file using a Python library (e.g., openpyxl) within the Lambda function, and saves the file directly to the network drive using the service account's credentials. Configure API Gateway to authenticate requests from Snowflake.
Answer: A
Explanation:
Option E provides a balance of security and efficiency. By creating a task that runs a stored procedure, converting data to CSV and using Java UDF to copy to an internal stage. A external process which is also monitoring the file and move to network directory using service account. It encapsulates logic within Snowflake and minimizes external dependencies. This approach avoids directly exposing Snowflake credentials to a third-party ETL tool or directly accessing the network drive from within Snowflake, which are security concerns. Option A involves writing directly to the network drive from within Snowflake, which may be complex to set up securely. Option B is generally not recommended due to external function overhead. Option C Introduces external dependency and Snowflake credentials needs to be managed carefully for the third party ETL tool. Option D is not possible since Snowpipe doesn't have capability to load into the network directory directly.
NEW QUESTION # 40
You are working with a table named 'PRODUCT DETAILS' that contains a 'PRICE column stored as a VARCHAR. The data in this column has inconsistencies, including leading/trailing spaces, currency symbols (e.g., '$', 'O'), and different decimal separators ('.' And ','). Additionally, some values are represented as 'N/A' or an empty string. You need to clean and validate this data to ensure the "PRICE' column can be safely converted to a NUMERIC data type. Choose the set of SQL transformations that will correctly clean the PRICE column. (Select all that apply)
- A. ```UPDATE PRODUCT_DETAILS SET PRICE = REPLACE(PRICE,',','.' );```
- B. ```sql UPDATE PRODUCT_DETAILS SET PRICE = TRIM(PRICE);```
- C. UPDATE PRODUCT_DETAILS SET PRICE = NULL WHERE PRICE IN ('N/A',");```
- D. ```sql UPDATE PRODUCT DETAILS SET PRICE = '[$, ]';```
- E. ```sql ALTER TABLE PRODUCT DETAILS ALTER COLUMN PRICE SET DATA TYPE NUMBER (10,2);```
Answer: A,B,C,D
Explanation:
This question requires multiple correct answers. Options A, B, C, and D are all necessary for cleaning the 'PRICE' column. Option A handles the 'N/A' and empty string values by setting them to NULL. Option B removes leading/trailing spaces using the 'TRIM' function. Option C removes currency symbols using 'REGEXP REPLACES. Option D standardizes the decimal separator by replacing commas with periods using REPLACE. Option E is not correct because it attempts to change the data type of the 'PRICE column, but the column is not yet cleaned to allow for successful conversion to a NUMERIC data type.
NEW QUESTION # 41
A data pipeline is failing intermittently, with the error logs indicating 'Insufficient compute resources'. You are tasked with collecting data to diagnose the root cause. What combination of Snowflake features and data collection strategies would be MOST effective in identifying if warehouse auto-scaling or query performance is the primary contributor to the issue?
- A. Monitor the Snowflake warehouse resource monitor metrics (e.g., QUEUED LOAD BYTES, EXECUTION TIME) using the Snowflake web interface or view, focusing on periods coinciding with pipeline failures.
- B. Increase the warehouse size significantly and observe if the pipeline failures cease.
- C. Create a new, larger warehouse and migrate the pipeline to it without analyzing the current warehouse's performance.
- D. Collect only error logs generated by the data pipeline to understand underlying problem.
- E. Analyze the average query execution time for all queries run by the pipeline using the QUERY_HISTORY view and compare it to the warehouse's average execution time. Additionally, check for queries spilling to local disk (using WAREHOUSE LOAD) during failure periods.
Answer: A,E
Explanation:
Options A and C provide a targeted approach. A allows tracking warehouse resource usage during the pipeline failures, identifying if the warehouse is reaching its limits. C helps identify slow-running queries contributing to resource exhaustion and detects queries using excessive local disk, indicating potential optimization opportunities. Increasing the warehouse size without analysis (B & D) is not cost-effective. Collecting only error logs (E) is not sufficient for a comprehensive diagnostic approach. It is too narrow and may not expose warehouse load issues and bottlenecks.
NEW QUESTION # 42
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?
- A. They improve data visibility for all users without restrictions.
- B. Both policies restrict data visibility for better security.
- C. Row access policies limit data visibility based on user privileges.
- D. Dynamic Data Masking doesn't affect data visibility in dashboards.
Answer: C
Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.
NEW QUESTION # 43
Why would a Data Analyst use a dimensional model rather than a single flat table to meet BI requirements for a virtual warehouse? (Select TWO).
- A. Dimensional modelling will improve query performance over a single table.
- B. Combining facts and dimensions in a single flat table limits the scalability and flexibility.
- C. Dimensions and facts allow power users to run ad-hoc analyses.
- D. Snowflake generally performs better with dimensional modelling.
- E. Dimensional modelling will save on storage space since it is denormalized.
Answer: B,C
Explanation:
In the field of data warehousing and business intelligence (BI), choosing the right data model is crucial for long-term maintainability and user accessibility. While a single flat table might seem simple initially, dimensional modeling (typically using Star or Snowflake schemas) provides distinct advantages for enterprise analytics.
1. Scalability and Flexibility (Option C)
Combining all attributes into a single flat table creates a highly rigid structure. Every time a new attribute is added to a dimension (e.g., adding a "Promotion Category" to a product), the entire flat table must be rewritten or altered, which is inefficient for large datasets. Furthermore, flat tables often contain redundant data, leading to "update anomalies" where a change in a dimension attribute must be propagated across millions of rows. A dimensional model separates changing business processes (Facts) from the context of those processes (Dimensions), allowing the schema to scale and evolve independently.
2. Ad-hoc Analysis for Power Users (Option D)
Dimensional models are specifically designed to be intuitive for business users and BI tools. By organizing data into Facts (measurable metrics) and Dimensions (descriptive attributes), power users can easily "slice and dice" data across different hierarchies. For example, a user can quickly run an ad-hoc query to compare "Total Sales" (Fact) by "Store Region" (Dimension) and "Calendar Month" (Dimension). This structure provides a predictable and standardized "language" for the data, making it easier for users to build their own reports without needing a Data Analyst to create a custom flat table for every specific request.
Evaluating the Distractors:
* Option A and E: These are common misconceptions. Modern cloud data warehouses like Snowflake are often highly optimized for wide "flat" tables due to columnar storage and sophisticated pruning. In many cases, a flat table may actually outperform a multi-table join (dimensional model) because it avoids the computational overhead of the join itself.
* Option B: This is factually incorrect. Flat tables are denormalized (repeating data), which generally takes more storage space. Dimensional modeling is a form of normalization that saves space by storing descriptive strings once in a dimension table rather than repeating them for every transaction in a fact table.
NEW QUESTION # 44
A key aspect of performing exploratory ad-hoc analyses is:
- A. Relying solely on predefined hypotheses
- B. Limiting data sources
- C. Flexibility in querying and data exploration
- D. Following a strict data model
Answer: C
NEW QUESTION # 45
In what ways do stored procedures differ from user-defined functions (UDFs) in SQL?
- A. Stored procedures and UDFs are interchangeable in SQL.
- B. Stored procedures can't execute repetitive tasks like UDFs.
- C. Stored procedures only handle basic arithmetic operations.
- D. UDFs allow custom-defined operations on data, extending SQL functionalities.
Answer: B
Explanation:
Stored procedures and UDFs differ in their ability to execute repetitive tasks.
NEW QUESTION # 46
Which action is crucial for identifying demographics and relationships during diagnostic analysis?
(Select all that apply)
- A. Analyzing isolated anomalies without considering relationships
- B. Ignoring statistical trends for focused analysis
- C. Considering relationships among data variables
- D. Examining demographic variations linked to anomalies
Answer: C,D
Explanation:
Analyzing demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.
NEW QUESTION # 47
When evaluating and selecting data for building dashboards, what factors should be considered for ensuring data relevance and usefulness? (Select all that apply)
- A. Filtering data based on irrelevant attributes
- B. Including all available data for comprehensive visualization
- C. Evaluating data based on business requirements
- D. Ignoring data complexities for simplicity in visualization
Answer: A,C
Explanation:
To ensure relevant and useful dashboards, data must be evaluated based on business requirements and filtered for irrelevant attributes.
NEW QUESTION # 48
A retail company uses Snowflake to store sales data'. They want to build a dashboard in Tableau to analyze regional sales performance. The sales data is stored in a table called 'SALES DATA' with columns 'REGION', 'PRODUCT CATEGORY, 'SALE AMOUNT, and 'SALE DATE. They want to optimize the Tableau dashboard's performance when querying Snowflake. Which of the following Snowflake features, when correctly implemented, will MOST effectively improve the query speed of the dashboard?
- A. Creating a standard Snowflake view directly querying 'SALES DATA' and connecting Tableau to that view.
- B. Using a stored procedure in Snowflake to calculate and store aggregated sales data in a separate table, and connecting Tableau to this aggregated table.
- C. Implementing Snowflake's Data Marketplace to source external sales data, which Tableau can then directly connect to without needing to access the company's 'SALES DATA'.
- D. Using Tableau's data extract feature to import all 'SALES DATA into a Tableau Hyper file and connecting the dashboard to the extract.
- E. Creating a materialized view on top of 'SALES_DATA' , pre-aggregating sales data by REGION' and 'PRODUCT_CATEGORY , and connecting Tableau to the materialized view.
Answer: E
Explanation:
Materialized views in Snowflake are designed to pre-compute and store the results of a query, significantly reducing the query execution time when the same query is run again. By pre-aggregating the sales data, the Tableau dashboard can retrieve the required aggregated data much faster than querying the entire table each time. Option A will still query the base table. Option C bypasses Snowflake entirely, which may not be desired. Option D is irrelevant to the problem stated. Option E involves a more complex setup and maintenance compared to materialized views, making it less optimal.
NEW QUESTION # 49
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