Updated Nov 21, 2025 Verified DAA-C01 dumps Q&As - 100% Pass [Q13-Q35]

Updated Nov 21, 2025 Verified DAA-C01 dumps Q&As - 100% Pass

New 2025 Latest Questions DAA-C01 Dumps - Use Updated Snowflake Exam

NEW QUESTION # 13
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

• A. Option E
• B. Option D
• C. Option B
• D. Option C
• E. Option A

Answer: C

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.

NEW QUESTION # 14
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. 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.
• B. 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.
• C. 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.
• D. 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.
• E. 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.

Answer: B,C

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 # 15
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

• A. Leveraging Snowflake's dynamic data masking and data classification capabilities to maintain data security and compliance while adhering to real-time data ingestion.
• B. Using a traditional ETL tool to extract, transform (calculate rolling average), and load the data into Snowflake in 15-minute intervals.
• C. Storing the raw data into Snowflake using Snowpipe in micro-batches and creating a VIEW that performs the rolling average calculation on-demand.
• D. Employing a stream processing framework (e.g., Apache Kafka) to ingest the data, perform the rolling average calculation using a tumbling window, and load the aggregated results into Snowflake.
• E. Using a scheduled task to query the API every minute and store the data directly into a Snowflake table with a materialized view calculating the rolling average.

Answer: D

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.

NEW QUESTION # 16
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?

• A. SELECT TRIM(REGEXP >', FROM COMMENTS;
• B. SELECT >', FROM COMMENTS WHERE
• C. SELECT >', comment_text) FROM COMMENTS;
• D. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
• E. SELECT >', FROM COMMENTS;

Answer: E

Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.

NEW QUESTION # 17
How does understanding and analyzing the Query Profile contribute to query optimization in Snowflake?

• A. Validates query result consistency
• B. Facilitates query planning and execution analysis
• C. Provides real-time data updates
• D. Limits query access for specific user roles

Answer: B

Explanation:
Analyzing the Query Profile aids in understanding query planning and execution, offering insights into optimizing query performance in Snowflake by identifying execution steps and bottlenecks.

NEW QUESTION # 18
What role does operationalizing data play in maintaining reports and dashboards for business requirements?

• A. Operationalizing data ensures consistent and efficient usage.
• B. It limits the usability of reports by narrowing down access.
• C. It restricts data updates, affecting dashboard accuracy.
• D. Operationalizing data complicates dashboard management.

Answer: A

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 19
Which action is crucial for identifying demographics and relationships during diagnostic analysis?
(Select all that apply)

• A. Analyzing isolated anomalies without considering relationships
• B. Examining demographic variations linked to anomalies
• C. Considering relationships among data variables
• D. Ignoring statistical trends for focused analysis

Answer: B,C

Explanation:
Analyzing demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.

NEW QUESTION # 20
You are creating a Snowsight dashboard to display the results of an A/B test on a website. You have the following tables: (columns: 'USER_ID', 'VARIANT' (VARCHAR, either 'A' or 'B'), 'CONVERSION' (BOOLEAN), 'TIMESTAMP') 'USER DEMOGRAPHICS' (columns: 'USER_ID, 'REGION', 'DEVICE) The stakeholders want to see the following visualizations: 1. Overall conversion rate for each variant. 2. Conversion rate for each variant broken down by region. 3. A table showing the statistical significance (p-value) of the difference in conversion rates between variants for each region, using a Chi-Square test. (Assume you have access to a stored procedure CHI SQUARE TEST(variant_a_conversions INT, variant_a_total INT, variant_b_conversions INT, variant b total INT) that returns the p-value.) Which combination of queries and Snowsight features will achieve the desired outcome with optimal performance and maintainability?

• A. Create a stored procedure that takes 'start_date' and 'end_date' as parameters, performs all calculations (conversion rates and Chi-Square tests), and returns three result sets for the visualizations. Use the stored procedure as the data source for the Snowsight dashboard.
• B. Create two views: 'VARIANT CONVERSION RATES': Calculates the overall and regional conversion rates. RESULTS': Executes for each region based on the view. Create three charts in Snowsight, using the views for the conversion rates and statistical significance table.
• C. Create a single view 'COMBINED_DATX that joins and ' USER_DEMOGRAPHICS'. Use this view in Snowsight to create all three charts. Use calculated fields in Snowsight to determine conversion rates and call the stored procedure for p-value within the calculated field.
• D. Create three separate charts using raw SQL queries for each visualizatiom Calculate the p-value outside of Snowflake using Python and load it into a new table. Use this new table to display a table chart with p-values.
• E. Create a task to aggregate the number of conversions and total views from the ab test data daily. Create views using the aggregated table to build the requested charts in Snowsight.

Answer: B

Explanation:
Option B is the most efficient and maintainable. Creating two views allows for clean separation of concerns and reusability. 'VARIANT CONVERSION RATES pre-calculates the conversion rates, making the statistical significance calculation in cleaner and more performant. The resulting Snowsight dashboard is then simple to build using these views. Option A introduces unnecessary complexity by involving external tools (Python) and creating a new table. Option C attempts to do too much within Snowsight's calculated fields, which is not ideal for complex calculations like calling stored procedures. Option D might be performant, but makes the data presentation inflexible and ties the data to this specific dashboard. Option E is good to have the data aggregated, however it depends on option B to create the dashboard with the aggregated data.

NEW QUESTION # 21
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

• A. Use the visit_timestamp)' function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter directly in the SQL query.
• B. Create a calculated field in Snowsight that extracts the hour from the 'visit_timestamp' and then group by that calculated field to calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
• C. Use the 'TO_CHAR(visit_timestamp, 'YYYY-MM-DD HH24')' function to group data by hour, but only use this option if 'visit_timestamp' is already stored as TEXT.
• D. Use the function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
• E. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.

Answer: A,E

Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.

NEW QUESTION # 22
A Snowflake table 'SALES_DATA' contains a 'TRANSACTION_ID' (VARCHAR), 'AMOUNT (VARCHAR), and 'TRANSACTION DATE (VARCHAR) column. Some 'TRANSACTION_ID' values are alphanumeric, others are purely numeric. The 'AMOUNT' column sometimes contains currency symbols ('$', ' ') or commas, and 'TRANSACTION DATE' is in 'MM/DD/YYYY' format. You need to perform the following transformations: 1. Extract only numeric 'TRANSACTION ID's. 2. Convert "AMOUNT' to a numeric type for calculations, removing currency symbols and commas. 3. Convert 'TRANSACTION DATE to a DATE type. Which of the following SQL queries effectively accomplishes these data type transformations in Snowflake?

• A. Option B
• B. Option D
• C. Option E
• D. Option C
• E. Option A

Answer: C

Explanation:
Option E is the most comprehensive and robust solutiom - It uses 'REGEXP LIKE to filter out non-numeric and the CASE statement, which is important because 'TRANSACTION_lD's will have both numeric and alphanumeric values. - The 'AMOUNT' column correctly uses 'REGEXP_REPLACE and 'TRY_CAST to handle multiple currency symbols and converts the values to DECIMAL. -is used which handles incorrect data in DATE conversion and return NULL in case of invalid 'TRANSACTION_DATE. Option A is incorrect because IS_INTEGER is not a standard built-in Snowflake function. Option B can cause errors if the TRANSACTION_ID cannot be converted to INTEGER after being checked with REGEXP LIKE. Option C's CAST statements can cause errors if there's any data that cannot be correctly CAST.

NEW QUESTION # 23
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. 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.
• B. 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.
• C. 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.
• D. 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.
• 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: B,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 # 24
You have two Snowflake tables: 'transactions' (containing transaction details with columns 'transaction id', 'customer id', 'amount' , 'transaction_date') and 'customer_demographicS (containing customer demographic information with columns 'customer_id', 'age' , gender' , 'location'). You need to enrich the 'transactions' table with customer demographics to analyze transaction patterns based on customer segments. What is the most efficient and scalable way to achieve this data enrichment in Snowflake, considering the 'transactions' table contains billions of rows?

• A. Export the tables to a data lake, perform the join using a Spark cluster, and then import the enriched data back into Snowflake.
• B. Using a standard INNER JOIN between the 'transactions' and 'customer_demographics' tables in a CREATE TABLE AS SELECT (CTAS) statement.
• C. Using Snowflake's materialized views to pre-compute the enriched data and refresh it periodically.
• D. Using Snowflake Streams and Tasks to incrementally enrich the 'transactions' table as new data arrives.
• E. Using a series of correlated subqueries to fetch the demographic information for each transaction record.

Answer: B,C,D

Explanation:
Options A, C, and D are efficient approaches. Option A (CTAS with INNER JOIN) is often the simplest and most performant for a one- time enrichment, leveraging Snowflake's query optimization. Option C (Materialized Views) provides pre-computed enriched data, ideal for frequent queries on the enriched dataset. Option D (Streams and Tasks) enables incremental enrichment, suitable for real-time or near real-time data updates. Option B (correlated subqueries) is generally inefficient and should be avoided with large datasets. Option E introduces unnecessary complexity by exporting data to a data lake.

NEW QUESTION # 25
What actions are typically involved in working with and querying data in Snowflake? (Select all that apply)

• A. Using randomization techniques
• B. Identifying and handling data anomalies
• C. Employing time travel for data retrieval
• D. Leveraging materialized views for aggregations

Answer: A,B,C,D

Explanation:
Working with Snowflake data involves identifying anomalies, using randomization, employing time travel for historical data retrieval, and utilizing materialized views for enhanced query performance.

NEW QUESTION # 26
A Snowflake data pipeline utilizes Snowpipe to ingest data from cloud storage into a staging table. After the data is ingested, a series of transformation tasks process the data and load it into a target table. Due to network connectivity issues, Snowpipe occasionally experiences delays in loading data, causing downstream tasks to fail. Which strategies can be implemented to monitor the latency and improve the resilience of this pipeline?

• A. Configure a Snowflake resource monitor to track the Snowpipe's credit consumption and trigger alerts when consumption exceeds a threshold.
• B. Implement a check within the transformation tasks to verify that the staging table contains data newer than a specific timestamp before proceeding with the transformation. Use ' on the staging table to get the last updated timestamp.
• C. Implement error handling within the transformation tasks to gracefully handle the case where the staging table is empty and log the error to a dedicated logging table.
• D. Increase the 'MAX CONCURRENCY parameter of the Snowpipe to improve the ingestion rate.
• E. Implement a retry mechanism within the transformation tasks that waits for a specified amount of time and then checks the staging table for new data before proceeding.

Answer: B,C,E

Explanation:
Option A is correct because checking the last updated timestamp allows the task to skip its execution or alert if data isn't timely. Option C is correct as it offers a resilient approach. Option E is also a correct since error logging is a critical part of resilience. Option B addresses throughput, not necessarily latency or resilience related to external issues, increasing concurrency does not guarantee latency improvements if connectivity is the issue. Option D only monitors credit consumption, not pipeline latency.

NEW QUESTION # 27
You have a stored procedure written in Python within Snowflake that needs to process a large dataset. The procedure's performance is critical, and you want to optimize it for speed. Which of the following strategies would be MOST effective in improving the performance of your Python stored procedure when dealing with large datasets in Snowflake? (Select TWO)

• A. Replacing all Python loops with recursive SQL queries to improve performance
• B. Leveraging Pandas DataFrames within the Python stored procedure to perform data manipulation tasks in a vectorized manner. Pandas provides efficient data structures and functions for data processing, which can significantly improve performance compared to using standard Python loops.
• C. Using the Snowflake Python Connector's 'execute_stream' method to stream data in chunks instead of loading the entire dataset into memory at once. This reduces memory consumption and improves performance, especially for very large datasets.
• D. Converting the Python stored procedure to a SQL stored procedure, as SQL is generally faster for data manipulation tasks within Snowflake due to its optimized query engine.
• E. Increasing the warehouse size for the Snowflake account where the stored procedure is running. A larger warehouse provides more compute resources, which can speed up data processing tasks, including Python stored procedures.

Answer: B,C

Explanation:
Options A and C are the most effective strategies. Option A, 'execute_stream' , is ideal for large datasets as it avoids loading everything into memory. Option C, leveraging Pandas DataFrames for vectorized operations, significantly speeds up data manipulation within Python. Option B is partially correct. SQL is often faster, but rewriting a complex Python procedure in SQL can be time-consuming and may not always be feasible or optimal. Option D, increasing the warehouse size, can help, but it's more of a general performance improvement rather than a specific optimization for Python stored procedures. Also it will cost more. E is incorrect as recursive SQL queries, while sometimes useful, aren't a general-purpose replacement for Python loops and can be less efficient for complex logic.

NEW QUESTION # 28
You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?

• A.
• B.
• C.
• D.
• E.

Answer: D

Explanation:
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.

NEW QUESTION # 29
How does leveraging partition pruning enhance query performance in Snowflake?

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

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 # 30
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

• A. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
• B. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
• C. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
• D. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.
• E. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.

Answer: C

Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

NEW QUESTION # 31
You are analyzing customer order data in Snowflake and need to determine if there is a statistically significant correlation between the number of items in an order ('ITEM COUNT) and the total order value CORDER VALUE'). You have a table named 'ORDERS' with columns 'ORDER ID', 'ITEM COUNT', and 'ORDER VALUE'. Which of the following Snowflake functions or methods, used in combination, would be the MOST appropriate and statistically sound for calculating the correlation coefficient between these two variables, taking into account the need to handle potential NULL values appropriately?

• A. Use 'AVG' and 'STDDEV_POP functions to calculate the mean and standard deviation for both and Then, manually compute the correlation coefficient using these statistics.
• B. First, replace NULL values in both 'ITEM COUNT and 'ORDER _ VALUE with 0 using 'COALESCE, then apply the 'CORR function.
• C. Use the 'CORR function directly on the 'ITEM_COUNT and 'ORDER_VALUE columns. No special handling is needed for NULL values as 'CORR automatically ignores them.
• D. Use a combination of 'WHERE clause to filter out rows where either 'ITEM COUNT or 'ORDER VALUE is NULL, and then apply the 'CORR function to the filtered data.
• E. Use 'QUALIFY Clause with 'CORR function to determine the coefficient values, and then apply statistical significance tests manually by calculating p-values.

Answer: D

Explanation:
The 'CORR function in Snowflake can calculate the Pearson correlation coefficient. However, NULL values can affect the result. Option E correctly handles this by explicitly filtering out rows containing NULL values in either column using a 'WHERE' clause, ensuring that the 'CORR function is applied only to complete pairs of data. Replacing NULL with zero can skew the distribution. Manual computation using AVG and STDDEV POP are more error prone and time taking.

NEW QUESTION # 32
A retail company suspects a sudden drop in sales in the 'Electronics' category. You, as a data analyst, need to perform a diagnostic analysis. Which of the following data collection strategies would be MOST effective to pinpoint the root cause, considering the limitations of Snowflake's cost optimization?

• A. Gather customer support tickets related to 'Electronics' purchases from the last month, focusing on complaints about product quality or delivery issues.
• B. Collect aggregated monthly sales data for the 'Electronics' category, comparing this year's figures to the previous year's.
• C. Extract a full data dump of all sales transactions across all categories and historical periods to identify any anomalies globally.
• D. Collect detailed sales data (product ID, price, discounts, region, customer segment, timestamps) for the 'Electronics' category for the last 3 months, focusing on daily and hourly trends.
• E. Execute a sampling query to extract 10% of the sales data of Electronic Category and compare it with previous period data.

Answer: A,D

Explanation:
Option A provides granular data for trend analysis, and option D brings qualitative data (support tickets) that can reveal underlying issues like product defects or shipping problems. Collecting ALL sales data (option B) is resource-intensive and inefficient for initial diagnostic analysis. Option C lacks the necessary granularity. Option E can be used to establish a baseline for comparison, but is not effecient due to sampling of data.

NEW QUESTION # 33
You have a large table 'WEB EVENTS with columns 'EVENT TIMESTAMP, 'USER ID', 'PAGE URL', and 'EVENT _ TYPE. You need to create a materialized view that efficiently calculates the daily unique user count for a specific set of 'PAGE URL' values. The 'WEB EVENTS table is frequently updated. Which of the following approaches would be MOST performant and scalable for this scenario?

• A. Ingest the daily unique user count data via a 3rd party tool into a new table and create a view using that table.
• B. Create a materialized view that directly selects 'COUNT(DISTINCT USER_ID)' grouped by ' and with filtering on the desired 'PAGE URL' values.
• C. Create a materialized view using a window function to calculate the running total of unique users each day, then extract the final value for each day.
• D. Create a materialized view that first calculates the total number of events for each user on each day and then aggregates that data to calculate the unique user count.
• E. Create a standard view that filters the 'WEB_EVENTS' table and calculates 'COUNT(DISTINCT USER_ID)' grouped by and 'PAGE URL'.

Answer: B

Explanation:
Calculating 'COUNT(DISTINCT directly in the materialized view is the most efficient approach. Pre-calculating unnecessary aggregates adds overhead. Standard views do not provide the performance benefits of materialized views. Window functions are generally less performant than direct aggregations in this scenario, and the new data can be calculated directly in Snowflake, avoiding dependency with 3rd party tools.

NEW QUESTION # 34
What effect do row access policies have on the creation of dashboards concerning user data visibility?

• A. Row access policies impact dashboard creation negatively.
• B. Row access policies offer unrestricted data visibility.
• C. They limit data visibility based on user privileges.
• D. Row access policies don't influence 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 # 35
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