Pass Snowflake DAA-C01 With ValidBraindumps Exam Dumps – Updated on Jul-2026 [Q12-Q33]

NEW QUESTION 12
You are a data analyst at a retail company. You need to calculate the total sales for each product category, but only for categories where the average unit price is above $50 and the number of sales transactions exceeds 1000. Which Snowflake SQL query would efficiently achieve this?

NEW QUESTION 13
You have created a forecasting model in Snowflake to predict customer churn based on historical data’. The model is named ‘CHURN FORECAST MODEL’. After running the forecast, you need to evaluate the model’s performance and understand its accuracy.
Which of the following methods or SQL commands can you use to accomplish this effectively?

Use the ‘SHOW MODELS command to view the model’s metadata, including accuracy metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Use the function to download the model’s internal representation and analyze it using external machine learning libraries.

Use the ‘DESCRIBE MODEL CHURN FORECAST MODEL’ command to retrieve model statistics such as the features used and their importance in the model.

There is no native way to evaluate forecasting model performance in Snowflake. You must manually calculate error metrics by comparing the forecast results with actual historical data using SQL queries.

Use the ‘SELECT FROM command to get a summary of the model’s performance, including error metrics. Additionally, manually compare the forecasted values to the actual values to calculate error metrics.

NEW QUESTION 14
How do automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

Automated tasks increase complexity in dashboard management.

They hinder scalability in reports and dashboards.

Automated tasks ensure consistency and reduce manual effort.

Repeatable tasks solely enhance data updates in dashboards.

NEW QUESTION 15
How do stored procedures contribute to data analysis efficiency in SQL?

They restrict data accessibility for security purposes.

They only facilitate basic data summarization.

Stored procedures can’t be used with UDFs.

Stored procedures enable custom and repetitive data operations, enhancing efficiency.

NEW QUESTION 16
A company stores web analytics data in a Snowflake table named ‘WEB EVENTS. This table includes a ‘USER ID column, a ‘TIMESTAMP’ column indicating when the event occurred, and a ‘EVENT TYPE column that captures the type of event (e.g., ‘page_view’, ‘add_to_cart’, ‘purchase’). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?

Using a correlated subquery to find the minimum and maximum timestamp for each user in the ‘WEB EVENTS’ table.

Using window functions such as FIRST _ VALUE and ‘LAST_VALUE partitioned by ‘USER_ID and ordered by ‘TIMESTAMP’ to find the first and last event times.

Creating a stored procedure that iterates through each user ID and finds the minimum and maximum timestamp using separate queries.

Using a lateral view combined with a table function to find the first and last event times.

Using a simple GROUP BY clause on ‘USER ID to find the minimum and maximum timestamp.

NEW QUESTION 17
You are building a dashboard to monitor the performance of a Snowflake data pipeline. This pipeline ingests data from various sources, transforms it, and loads it into target tables. You want to visualize the overall pipeline latency, including the time spent in each stage (ingestion, transformation, loading). You have access to event logs that capture the start and end timestamps for each stage of each pipeline run. The logs are stored in a Snowflake table named ‘PIPELINE LOGS’ with columns: ‘PIPELINE RUN (VARCHAR), ‘STAGE_NAME’ (VARCHAR), ‘START_TIMESTAMP’ (TIMESTAMP_NU), ‘END_TIMESTAMP (TIMESTAMP_NTZ). Which visualization type and query construct provides the MOST effective way to visualize the latency of each stage within each pipeline run, allowing for easy identification of bottlenecks?

A bar chart showing the average latency for each stage, calculated using the aggregate function and grouping by ‘STAGE_NAME’.

A box plot visualizing the distribution of latencies for each stage, generated using a query with window functions to calculate percentiles and outliers.

A Gantt chart displaying the start and end times of each stage for each pipeline run, created using a query that calculates the duration of each stage using ‘TIMESTAMPDIFF()’.

A line chart showing the total latency of each pipeline run over time, calculated using the ‘SUM()’ aggregate function and grouping by and date.

A heatmap showing correlation between start_timestamp and end_timestamp for each pipeline run for all stages.

NEW QUESTION 18
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?

Website clickstream data stored in AWS S3 buckets in Parquet format.

Database containing HR employee data.

IoT sensor data containing temperature readings.

Salesforce data containing customer interactions and sales opportunities.

Social media activity data ingested via a third-party API and stored in a relational database.

NEW QUESTION 19
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;

SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;

SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;

SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;

SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;

NEW QUESTION 20
How do constraints contribute to ensuring data integrity in Snowflake?

They enforce data security measures.

Constraints ensure redundant data storage.

Constraints restrict data import from external sources.

They impose rules for maintaining data accuracy and consistency.

NEW QUESTION 21
You are analyzing sales data for a retail company. The ‘sales’ table contains columns ‘product id’ (INT), ‘sale date’ (DATE), and ‘sale_amount’ (NUMBER). You need to calculate the percentage contribution of each product’s sales to the total sales on each day. You want the result to include ‘sale date’, ‘product_id’, ‘sale_amount’, ‘daily total’ , and ‘percentage_contribution’. Which of the following Snowflake queries achieves this correctly?

Option A

Option B

Option C

Option D

Option E

NEW QUESTION 22
A financial institution uses Snowflake to store customer transaction data’. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the ‘TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?

Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.

Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.

Increase the warehouse size to X-Large and rely on Snowflake’s query optimization engine without any changes to the data model or update process.

Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.

Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues

NEW QUESTION 23
Which of the following is a key step in data preparation?

Data normalization

Model deployment

Algorithm selection

Visual analysis

NEW QUESTION 24
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?

Limited external data sources

High data redundancy

Inability to access marketplace data

Data format compatibility issues

NEW QUESTION 25
A financial institution uses Snowflake to store transaction data’. They observe an unexpected spike in fraudulent transactions during a specific weekend. The ‘TRANSACTIONS table contains columns like ‘transaction id’, ‘transaction date’, ‘amount’, ‘merchant_category’, ‘user_id’, and ‘is_fraudulent. Which of the following approaches, leveraging Snowflake’s capabilities, would be MOST effective in diagnosing the potential causes of this fraud spike?

Use a machine learning model (deployed as a Snowflake user-defined function) to re-score all transactions from the affected weekend based on historical patterns and identify potentially misclassified transactions.

Analyze transaction patterns by ‘merchant_category’ and ‘user_id’ before, during, and after the spike to identify any unusual concentrations or emerging fraudulent schemes. Consider using ‘QUALIFY’ clause to filter top N merchant_category by transaction amount.

Join the ‘TRANSACTIONS table with a table containing external threat intelligence data (e.g., IP address reputation, known fraudulent merchants) to identify transactions originating from suspicious sources.

Create cohorts of users based on transaction behavior before the spike and compare their transaction patterns during the spike to identify anomalies within specific user groups. Use window functions like ‘FIRST_VALUE to define cohorts.

Only check the transaction details, like amount and the users involved, and assume one of the users triggered those Fraud Trasanctions.

NEW QUESTION 26
A Data Analyst wants to transform query results. Which transformation option will incur compute costs?

Showing a thousand separator for numeric columns.

Sorting a column by using the column options.

Increasing or decreasing decimal precision.

Formatting date and timestamp columns.

NEW QUESTION 27
You are tasked with creating a dashboard to visualize customer churn. You have a Snowflake table named ‘CUSTOMER DATA’ with columns ‘CUSTOMER ID, ‘JOIN DATE, ‘LAST ACTIVE DATE, ‘REVENUE’, and CHURNED’ (BOOLEAN). You want to present a cohort analysis showing the retention rate of customers over time, grouped by their join month. Which of the following approaches using SQL and visualization techniques is the MOST effective for creating this cohort analysis visualization for a dashboard using only snowflake?

Create a single SQL query that calculates the retention rate for each cohort (join month) using window functions to count active customers in each subsequent month. Then, display the results in a bar chart showing the retention rate for each cohort over time.

Create a stored procedure that iterates through each join month, calculates the retention rate for each subsequent month, and stores the results in a temporary table. Then, use a charting library integrated with your dashboard to visualize the data from the temporary table as a heatmap or retention table.

Use a sequence to generate a series of dates representing the months since joining, then use conditional aggregation to calculate the number of customers retained in each month for each cohort. Visualize this data using a line chart or heatmap.

Create a series of SQL queries, one for each cohort, to calculate the retention rate for each subsequent month. Combine the results from these queries in your dashboard and display them using a line chart.

Export the data to a BI tool to make a visualization.

NEW QUESTION 28
How can automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

They solely increase the complexity of dashboard management.

They limit the scalability of dashboards and reports.

Repeatable tasks hinder data updates in dashboards.

Automated tasks ensure consistency and reduce manual effort.

NEW QUESTION 29
While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the ‘Join’ operation between two large tables, ‘transactions’ and ‘customers’. The join is performed on the ‘customer _ id’ column. Which of the following are potential strategies to optimize the join performance?

Ensure that the ‘customer_id’ column has identical data types in both tables.

Cluster both tables on the ‘customer id’ column.

Broadcasting the smaller table to all compute nodes.

Rewrite the query to use a ‘LATERAL FLATTEN’ function instead of a ‘JOIN’.

Increase the size of the virtual warehouse.

NEW QUESTION 30
You have a Snowflake table ‘CUSTOMER ORDERS with columns ‘CUSTOMER ID’, ‘ORDER DATE, and ‘ORDER AMOUNT. You need to calculate the cumulative sum of ‘ORDER AMOUNT’ for each customer, ordered by ‘ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there’s a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?

Option A

Option B

Option C

Option D

Option E

NEW QUESTION 31
You’re tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called ‘ETL LOGS’ with columns ‘end_time’, and ‘error_flag’ (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

Option A

Option B

Option C

Option D

Option E

NEW QUESTION 32
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: ‘PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) ‘SALES’ (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) ‘INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

Create a single, wide denormalized table containing all the necessary data from the ‘PRODUCTS, ‘SALES, and ‘INVENTORY tables using JOINs. Build the dashboard directly on this table.

Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.

Utilize Snowflake’s search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.

Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.

Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.

NEW QUESTION 33
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?

Using LATERAL FLATTEN to unnest the ‘attributes’ and then using a CASE statement to pivot the data.

Creating a view with direct JSON path accessors (e.g., for each desired attribute.

Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.

Creating a new table with a ‘VARIANT column for the attributes and performing transformations in a BI tool.

Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.

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Pass Snowflake DAA-C01 With ValidBraindumps Exam Dumps – Updated on Jul-2026 [Q12-Q33]

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