[Aug-2026] DAA-C01 Dumps With 100% Verified Q&As - Pass Guarantee or Full Refund [Q30-Q47]

[Aug-2026] DAA-C01 Dumps With 100% Verified Q&As - Pass Guarantee or Full Refund

Pass Snowflake DAA-C01 Exam With Practice Test Questions Dumps Bundle

NEW QUESTION # 30
You are analyzing website traffic data in Snowflake and notice a sudden drop in page views from a specific country (Country A) starting last month. You have access to the 'WEBSITE TRAFFIC' table with columns: 'date' , 'country', 'page_viewS, 'device_type'. Which of the following queries and techniques would be MOST effective in identifying the potential cause of this anomaly?

• A. Run a simple 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -3, GROUP BY date ORDER BY date;' to visualize the trend and confirm the drop.
• B. Analyze 'page_viewS by 'device_type' for Country A before and after the drop to see if the drop is concentrated in a specific device type (e.g., mobile, desktop). Use 'CASE statement within the 'GROUP to categorize time periods.
• C. Execute 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -1, CURRENT DATE());' and manually inspect the data for suspicious patterns.
• D. Use a statistical anomaly detection function (e.g., moving average) on the 'page_views' for Country A and compare against other countries to identify if the drop is specific to Country A. Consider using 'LAG' function with 'OVER' clause to calculate the moving average.
• E. Join the 'WEBSITE_TRAFFIC' table with a table containing marketing campaign data (MARKETING CAMPAIGNS') on 'date' and 'country' to see if any marketing campaigns were paused or modified in Country A around the time of the drop. Consider using 'LEFT JOIN' to not lose traffic data.

Answer: B,D,E

Explanation:
Options B, C, and D are the most effective. B uses statistical methods to identify the anomaly, C investigates potential external factors (marketing campaigns), and D explores internal segments (device types). Option A is a basic check but doesn't identify causes. Option E is not scalable and inefficient for large datasets. Using a combination of statistical analysis, external data integration, and segmentation provides a comprehensive diagnostic approach.

NEW QUESTION # 31
How do Snowsight's data loading capabilities impact data ingestion?

• A. Snowsight facilitates streamlined data import from various sources.
• B. It limits data ingestion to structured formats only.
• C. Snowsight enables real-time data ingestion.
• D. Data loading is restricted to batch processing only.

Answer: A

Explanation:
Snowsight allows streamlined data import from various sources, enhancing data ingestion capabilities.

NEW QUESTION # 32
Identify the correct action involved in performing an exploratory ad-hoc analysis.

• A. Utilizing ad-hoc queries to examine patterns and anomalies
• B. Relying solely on predefined queries without exploration
• C. Focusing on established trends without investigating anomalies
• D. Analyzing only a small fraction of the available data

Answer: A

Explanation:
Ad-hoc analysis involves using queries to explore patterns and anomalies within data, deviating from predefined routines.

NEW QUESTION # 33
You are designing a data ingestion pipeline to collect clickstream data from a high-traffic e-commerce website. You anticipate a daily data volume that fluctuates significantly, ranging from 5 TB to 20 TB. To optimize Snowflake costs and ensure efficient data loading, which combination of Snowflake features and ingestion methods would be MOST effective for identifying the accurate data volume before the data is fully loaded into a production table?

• A. Use Snowflake Tasks to schedule a Python script that reads file metadata (size, number of files) from the cloud storage location using the cloud provider's SDK (e.g., bot03 for AWS S3, azure-storage-blob for Azure Blob Storage). Store the collected metadata in a Snowflake table. Then, use SQL queries to calculate the total data volume.
• B. Utilize a cloud-based message queue (e.g., AWS SQS, Azure Queue Storage) to buffer incoming data. Implement a separate Snowflake task to periodically query the message queue's approximate message count and size. Combine this with Snowsight monitoring of storage costs for the raw data stage.
• C. Rely solely on Snowsight's query history and data loading metrics after the data has been loaded into Snowflake, adjusting future ingestion parameters based on historical data.
• D. Use Snowflake's Resource Monitors to track credit usage during data loading. Configure a data pipeline using Snowpipe with auto-ingest enabled. Rely solely on Snowflake's cost estimation tools to predict volume.
• E. Implement Snowpipe with pre-validation using a data transformation tool like dbt Core. Create an external function to execute a sampling query against a percentage of files in cloud storage and estimate the total volume. Load a smaller sample data to a staging table and then use 'TABLE SIZE' to get the actual size and scale it up.

Answer: A

Explanation:
Option D is the most effective because it directly measures the volume of data before it is fully loaded into Snowflake, allowing for accurate volume identification and cost optimization. Using cloud provider SDK to check the data volume will help to estimate cost and volume.

NEW QUESTION # 34
When summarizing large data sets using Snowsight dashboards, how do they facilitate exploratory analysis?

• A. Snowsight dashboards provide visual representations aiding in quick comprehension.
• B. Snowsight dashboards limit data representation options for large data sets.
• C. They offer detailed textual summaries instead of visual representations.
• D. They can't efficiently handle large data sets for summarization.

Answer: A

Explanation:
Snowsight dashboards provide visual representations aiding in quick comprehension of large data sets.

NEW QUESTION # 35
You have a Snowflake table called 'PRODUCT SALES' with columns 'PRODUCT ID (INT), 'SALE DATE' (DATE), and 'SALES AMOUNT' You want to implement a data integrity rule to prevent duplicate records based on 'PRODUCT ID and 'SALE DATE. Which of the following methods provides the most effective way to achieve this in Snowflake, and why?

• A. Add a composite UNIQUE constraint on 'PRODUCT ID' and 'SALE DATE. Snowflake will automatically prevent the insertion of duplicate rows during data loading or insertion.
• B. Implement data validation within your ETL pipeline before loading data into Snowflake to prevent duplicates from entering the table. This approach keeps the table clean from the start.
• C. Create a view that filters out duplicate records using 'ROW NUMBER()' and partitioning by 'PRODUCT ID and 'SALE DATE. This guarantees data integrity during query execution.
• D. Create a user defined function (UDF) that checks for the existance of data before insert. If data already exist, don't insert it.
• E. Create a stored procedure that runs periodically to identify and delete duplicate records based on 'PRODUCT and 'SALE DATE'. This approach fixes integrity issues reactively.

Answer: A,B

Explanation:
Options C and D provide the most effective methods. A composite UNIQUE constraint directly prevents duplicate insertions at the table level. Validating in the ETL pipeline (D) prevents duplicates before they even reach the database. A view (A) only masks the issue, and a stored procedure (B) is reactive and doesn't prevent duplicates from being inserted in the first place. A UDF could be helpful but is not the BEST option for this scenario.

NEW QUESTION # 36
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

• A. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.
• B. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.
• C. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
• D. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.
• E. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.

Answer: D

Explanation:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

NEW QUESTION # 37
A Data Analyst is creating a Snowsight dashboard from a shared worksheet. What happens to the access and permissions of the users who initially had sharing privileges on the worksheet?

• A. The original users gain additional access to the worksheet.
• B. The original users temporarily lose access but regain it once the dashboard is created.
• C. The original users lose access to the worksheet, their permissions on the worksheet are revoked.
• D. The original users retain access and permissions on the worksheet.

Answer: C

Explanation:
When working within the Snowsight interface, the transition from a standalone worksheet to a dashboard component involves a change in how the underlying SQL and its associated metadata are managed. When a worksheet is converted or used to create a dashboard, the ownership and sharing model shifts to the dashboard level.
According to Snowflake's documentation on Snowsight collaboration, when a user creates a dashboard from a worksheet that was previously shared with others, the original worksheet's individual share settings are essentially superseded by the dashboard's own permissions. In many workflow scenarios within the UI, once the worksheet is finalized into a dashboard tile, the direct, independent access to that specific worksheet is severed for the original "sharees." Their permissions on that specific worksheet are revoked to prevent conflicting edits between the standalone version and the dashboard-integrated version.
Evaluating the Options:
* Option A is incorrect because Snowsight manages the lifecycle of worksheets used in dashboards as part of the dashboard object; independent sharing of the underlying worksheet is typically disabled or revoked.
* Option B and C are distractors; there is no mechanism in Snowsight that grants "additional" access or
"temporary" loss during the creation process.
* Option D is the 100% correct answer based on the SnowPro Data Analyst standard regarding Snowsight object ownership and sharing lifecycle. To allow others to see the work, the Analyst must now share the Dashboard itself, rather than relying on the previous worksheet-level permissions. This ensures a "single source of truth" for the visualization's logic.

NEW QUESTION # 38
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. 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.
• B. 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.
• C. 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.
• D. 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.
• E. 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.

Answer: E

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 # 39
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

• A. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
• B. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
• C. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
• D. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
• E. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.

Answer: A,D

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

NEW QUESTION # 40
You are tasked with validating the 'SALES DATA' table containing sales records. One of the columns, 'SALE AMOUNT', is defined as VARCHAR, but it should be a NUMERIC. Some rows contain non-numeric characters and NULL values represented as the string 'NULL'. You need to identify rows that will cause errors when casting 'SALE AMOUNT to NUMERIC, and replace these rows with valid values using Snowflake SQL. Which of the following SQL statements, when executed in sequence, effectively identifies and corrects problematic 'SALE AMOUNT' values? Note: Assume the "REPLACE INVALID CHARACTERS' UDF correctly replaces non- numeric characters with empty strings.

• A.
• B.
• C.
• D.
• E.

Answer: B

Explanation:
Option D correctly addresses all aspects of the problem. First, it replaces 'NULL' string values with actual NULLs. Then, it creates a temporary table, 'INVALID SALES, containing rows where 'SALE AMOUNT, after removing invalid characters, cannot be converted to a DECIMAL. Finally, it updates these invalid rows in the original table with a default value of '0'. TRY_TO_DECIMAL is important for handling decimal values that cannot be converted

NEW QUESTION # 41
In data modeling for BI requirements, when is it preferable to use a flattened data set instead of a data model?

• A. For situations requiring high data normalization
• B. For quick and simple data exploration
• C. For scenarios necessitating extensive data transformations
• D. For complex data analysis needs

Answer: B

Explanation:
Flattened data sets are suitable for quick and simple data exploration due to their simplified structure, facilitating easy access and analysis.

NEW QUESTION # 42
When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)

• A. Data source availability
• B. Business requirements
• C. Volume of data
• D. Data collection tool limitations

Answer: B,C

Explanation:
Assessing data collection frequency involves considering business requirements and the volume of data necessary for analysis.

NEW QUESTION # 43
You are tasked with creating a dashboard to visualize website traffic data'. The data is stored in a Snowflake table 'WEB EVENTS' with the following columns: 'EVENT_ID', 'USER_ID', 'EVENT_TIMESTAMP', 'PAGE_URL', 'SESSION_ID', and 'DEVICE_TYPE. You need to calculate the average session duration for each device type, but the 'WEB EVENTS' table only contains individual events, not session start and end times. Assume a session is defined by a unique 'SESSION ID' for each 'USER ID. You are given a window function to calculate the start time and end time for a given 'SESSION ID': First_value and last_value. What steps are crucial to calculate average session duration by 'DEVICE TYPE?

• A. Calculate the sum of all 'EVENT TIMESTAMP' values for each 'USER ID. Group by 'DEVICE_TYPE and calculate the average.
• B. Simply group by 'DEVICE_TYPE and calculate the average ' EVENT_TIMESTAMP'
• C. Calculate the difference between the maximum and minimum 'EVENT _ TIMESTAMP' for each ' USER_ID. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
• D. Calculate median of event timestamp using percentile_cont. The calculate difference between median and max timestamps.
• E. Use window functions to determine the start and end 'EVENT TIMESTAMP for each 'SESSION ID. Calculate the difference between these start and end times. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.

Answer: E

Explanation:
Option B correctly identifies the steps to calculate session duration. First determine Session Start and End time. 1) Use window functions to identify the start and end timestamp for a given SESSION ID. 2) Calculate the difference between these timestamps. 3) Group by device type to arrive at the averages for session duration across the device_type. Option A is incorrect as it does not represent the session duration which needs to be calculated for a given Session_ID.

NEW QUESTION # 44
A retail company wants to understand the relationship between promotional campaigns and sales uplift across different store locations and product categories. You have the following Snowflake tables: 'SALES': 'transaction id', 'store id', 'product_category', 'sale date', 'sale_amount' 'PROMOTIONS': 'promotion id', 'store id', 'product category', 'promotion start date', promotion_end_date', 'discount_percentage' Which analytical approach and corresponding SQL query would be MOST effective in determining if specific promotional campaigns consistently result in a statistically significant sales uplift, considering potential variations across different store locations and product categories? Assume you want to compare sales during the promotion period to a control period (before the promotion). (Select TWO)

• A. Run a series of AIB tests by randomly assigning different discount percentages to different stores and product categories during the promotion period and track the resulting sales uplift, using a Mann-Whitney U test for statistical significance.
• B. Use a simple linear regression model in a Snowflake UDF (User-Defined Function) to predict sales based on the presence or absence of a promotion, without accounting for store or product category fixed effects.
• C. Calculate the average sales amount for each store and product category during promotion periods and compare it to the overall average sales amount across all periods using a t-test or similar statistical test to determine significance. This would involve exporting data into a statistical tool.
• D. Create a complex query that joins SALES and PROMOTIONS and calculates the percentage increase in sales during the promotion period compared to the average sales for the same store and product category in the 3 months prior, ignoring the potential for seasonality and other confounding factors.
• E. Employ a difference-in-differences (DID) approach, comparing the change in sales from the control period to the promotion period for the 'treatment group' (stores/product categories with promotions) relative to the change for a 'control group' (stores/product categories without promotions). This requires careful identification of suitable control groups.

Answer: C,E

Explanation:
Options A and C are the most effective. Option A utilizes t-tests to assess the statistical significance of sales during promotion periods versus the overall average. Combining statistical analysis with Snowflake data extraction provides insightful results. Option C proposes the Difference-in-Differences (DID) approach which is very effective. It uses a control group to account for external factors that may have also influenced sales. Comparing treated (promotion stores) and controlled store to find the diff in diff provides statistically significant evidence. Options B and E are less rigorous. B doesn't account for important fixed effects and E doesn't consider seasonality and confounding factors. Option D suggests an AIB test which is not practical as sales can not be assigned randomly during a promotion.

NEW QUESTION # 45
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_LENGTH
• B. ST_DISTANCE
• C. HAVERSINE
• D. ST_HAUSDORFFDISTANCE

Answer: B

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.
?

NEW QUESTION # 46
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. Create a new, larger warehouse and migrate the pipeline to it without analyzing the current warehouse's performance.
• C. Collect only error logs generated by the data pipeline to understand underlying problem.
• D. Increase the warehouse size significantly and observe if the pipeline failures cease.
• 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 # 47
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