Latest Snowflake DAA-C01 PDF and Dumps (2026) Free Exam Questions Answers [Q15-Q36]

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Latest Snowflake DAA-C01 PDF and Dumps (2026) Free Exam Questions Answers

Pass Your SnowPro Advanced DAA-C01 Exam on May 23, 2026 with 67 Questions

NO.15 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

NO.16 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.

NO.17 When handling erroneous data in Snowflake, what approaches can be employed to effectively address data anomalies and ensure data integrity? (Select all that apply)

Dropping all erroneous records

Using error tables to capture and analyze erroneous data

Performing data transformations without error handling

Utilizing clones for isolated error resolution

NO.18 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.

NO.19 The following code is run:

Then this statement is executed:

What will be the output of this statement?

To determine the correct output, a Data Analyst must understand the behavior of the ILIKE operator, wildcard characters, and the ESCAPE clause in Snowflake.
1. Pattern Matching with ILIKE: The ILIKE operator performs case-insensitive pattern matching. The provided pattern is ‘p%^_j%’.
* p: The string must start with the letter ‘P’ or ‘p’.
* %: This wildcard matches any sequence of zero or more characters.
* ^_: The ESCAPE ‘^’ clause identifies the caret symbol as an escape character. This means the underscore (_) immediately following it is treated as a literal character rather than its usual wildcard function (which matches any single character).
* j: The letter ‘J’ or ‘j’ must follow the literal underscore.
* %: Matches any trailing sequence of characters.
2. Evaluating the Data: Applying this logic to the values in the cert_dem table:
* ‘Peter*John’: While it starts with ‘P’ and contains ‘J’, it lacks the required literal underscore. No match.
* ‘Peter John’: Contains a space instead of an underscore. No match.
* ‘Peter_John’: Starts with ‘P’, contains a literal underscore, and is followed by ‘J’. Match.
* ‘Peter_john’: Because the operator is ILIKE (case-insensitive), the lowercase ‘j’ is accepted. Match.
* null: Comparisons with NULL using LIKE or ILIKE always return NULL (unknown), so it is excluded from the results.
3. Ordering the Results: The query includes ORDER BY 1, which sorts the results alphabetically based on the first column. Between ‘Peter_John’ and ‘Peter_john’, the standard sort order typically places uppercase before lowercase in most Snowflake collations.
Evaluating the Options (image_829269.png):
* Option A incorrectly excludes the case-insensitive match.
* Option B incorrectly identifies values that lack the literal underscore.
* Option D incorrectly includes values that do not meet the escaped underscore requirement.
* Option C is the 100% correct result set, displaying both versions of ‘Peter_John’ that contain the literal underscore.

NO.20 What distinguishes Materialized views from Secure views in the context of data analysis?

Secure views provide a precomputed snapshot of data, unlike Materialized views.

Materialized views restrict data access for security purposes, unlike Secure views.

Materialized views enhance data security, while Secure views offer improved query performance.

Secure views provide enhanced data security without precomputing data.

NO.21 You are tasked with analyzing website clickstream data stored in a Snowflake table called ‘clickstream_events’. Each row represents a click event and contains a ‘session_id’ , and a ‘properties’ column of type VARIANT that stores key-value pairs related to the event (e.g., ‘(page’: ‘[product/123’, ‘element’: You need to extract the ‘page’ and ‘element’ values from the ‘properties’ column and identify the most common ‘page’-‘element’ combinations for each ‘session_id’. Furthermore you need to limit the results of your data to the top 5 pages element pair. How can this task be accomplished using Snowflake table functions and analytical functions?

Use LATERAL FLATTEN to extract the keys and values from the ‘properties’ column, then use GROUP BY ‘session_id’, ‘key’, ‘value’ and COUNT( ) to find the most frequent combinations.

Use multiple LATERAL FLATTEN calls, one for ‘page’ and one for ‘element’, then JOIN the results on and use QUALIFY ROW_NUMBER() OVER (PARTITION BY ‘session_id’ ORDER BY COUNT( ) DESC) 5.

Extract ‘page’ and ‘element’ using ‘properties:page’ and ‘properties:element’ directly in the SELECT statement, then use GROUP BY ‘session_id’, ‘page’, ‘element’ and QUALIFY ROW NUMBER() OVER (PARTITION BY ‘session_id’ ORDER BY COUNT( ) DESC) 5.

Create a UDF to parse the ‘properties’ VARIANT and return a table with ‘page’ and ‘element columns, then JOIN this UDF’s output with the original table and use QUALIFY ROW NUMBER() OVER (PARTITION BY ‘session_id’ ORDER BY COUNT( ) DESC) 5.

First create a view that flattens the JSON column using LATERAL FLATTEN, then select from this view to perform the group by and ranking operations.

NO.22 What complexities might arise when identifying and resolving data import errors in Snowflake?
(Select all that apply)

Handling only specific error types

Identifying error sources

Analyzing error logs for resolution

Resolving data inconsistencies

NO.23 You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

Use Snowflake’s dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.

Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.

Utilize Snowflake’s caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.

Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.

Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.

NO.24 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)

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.

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.

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.

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.

Replacing all Python loops with recursive SQL queries to improve performance

NO.25 When cleaning data, what role does using clones play in specific use-cases?

Clones help preserve original data for audit purposes only

Clones slow down the data cleaning process significantly

Clones aid in isolating and resolving data anomalies

Clones are unnecessary for data cleaning tasks

NO.26 You are tasked with designing a Snowflake data model for a system that tracks changes to product information. You need to store both the current and historical states of the ‘PRODUCTS’ table. Which of the following strategies, regarding primary keys and table structures, is the MOST appropriate for efficiently querying the current product state and historical changes, considering best practices for Snowflake and minimizing storage costs? Select all that apply.

Create two tables: with ‘PRODUCT_ID as the primary key, storing the current product information, and ‘PRODUCTS_HISTORY’ with ‘PRODUCT ID’, ‘EFFECTIVE DATE, and ‘END DATES columns, where the combination of ‘PRODUCT and ‘EFFECTIVE DATE’ serves as a composite key. Use a scheduled task to copy data from ‘PRODUCTS_CURRENT to ‘PRODUCTS_HISTORY whenever a product is updated.

Create a single table, ‘PRODUCTS’, with columns ‘PRODUCT_ID (primary key), ‘PRODUCT_NAME’, ‘PRODUCT_DESCRIPTION’, ‘EFFECTIVE_DATE’, and ‘END_DATE. Whenever a product attribute changes, insert a new row with the updated attribute values and the new ‘EFFECTIVE_DATE , setting the ‘END_DATE’ of the previous record to the ‘ EFFECTIVE_DATE of the new record.

Create a single table, ‘PRODUCTS’, with ‘PRODUCT_ID as the primary key and a ‘JSON’ column to store the historical product data. Use a stored procedure to update the ‘ JSON’ column whenever a product attribute changes.

Create a single table, ‘PRODUCTS’, with columns ‘PRODUCT_ID (primary key), ‘PRODUCT NAME, ‘PRODUCT DESCRIPTION’, ‘EFFECTIVE DATE and ‘END_DATE. Implement clustering on ‘PRODUCT_ID’ and ‘EFFECTIVE_DATE’.

Create a single table, ‘PRODUCTS’, with columns ‘PRODUCT ID, ‘PRODUCT NAME’, ‘PRODUCT DESCRIPTION’, ‘EFFECTIVE DATE, and ‘END_DAT Add a sequence to act as a surrogate key and define this sequence as the primary key.

NO.27 When planning for data volume collection, what is an important consideration to ensure scalability and performance?

The physical location of data

The types of data visualization tools used

The data processing capabilities

The expected growth rate of data

NO.28 A Data Analyst is working with three tables:

Which query would return a list of all brokers, a count of the customers each broker has. and the total order amount of their customers (as shown below)?

NO.29 When evaluating and selecting data for building dashboards, what factors should be considered for ensuring data relevance and usefulness? (Select all that apply)

Ignoring data complexities for simplicity in visualization

Evaluating data based on business requirements

Including all available data for comprehensive visualization

Filtering data based on irrelevant attributes

NO.30 How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?

Offers options to alter query execution plans

Limits query execution time

Provides real-time data updates

Enables querying of historical data versions

NO.31 You are tasked with loading data from a REST API that returns data in a deeply nested JSON format. The API requires authentication via an API key passed in the header. The JSON response contains an array of objects, each representing a customer order. You need to load only orders placed in the last 7 days. Which of the following steps and techniques would you use in Snowflake to achieve this?

Use a Python UDF to call the REST API, extract the relevant data, and load it into a Snowflake table.

Create an external function to call the REST API using API Integration object, filter the orders based on the timestamp within the UDF, and return the filtered data to Snowflake to be loaded into a table. Utilize secure secrets for API Key Storage.

Use Snowflake’s ‘COPY INTO’ command with a preprocessor function to transform the JSON data and filter orders placed in the last 7 days. Store the API key in the preprocessor function.

Use Snowpipe with a continuous data loading pipeline, configure an external function to authenticate and load data into a temporary table then execute merge statement to apply it to the main table

Create a task with ‘SYSTEM$STREAM’ object to extract the data from API and apply it into Snowflake tables. Use ‘SECRET object to store API Key.

NO.32 When maintaining reports and dashboards, why is it crucial to configure subscriptions and updates?

They complicate dashboard management without any added benefits.

Configuring these features hampers dashboard usability.

They limit data accessibility for effective dashboard usage.

Subscriptions and updates ensure timely information delivery.

NO.33 There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?

Table_b has more rows than Table_a.

Table_b has a row-access policy defined.

A multi-cluster warehouse was used to run Query 1.

The USE_CACHED_RESULT was set to FALSE before running Query 2.

NO.34 You are working with a table ‘ORDERS’ containing order data, and a table ‘CUSTOMER SEGMENTS containing customer segment information. The ‘ORDERS’ table has columns ‘ORDER ID’, ‘CUSTOMER ID, and ‘ORDER AMOUNT’. The ‘CUSTOMER SEGMENTS’ table has columns ‘CUSTOMER ID’, ‘SEGMENT ID’, and ‘SEGMENT NAME’. You need to create a query that enriches the ‘ORDERS’ table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include ‘ORDER ID, ‘ORDER AMOUNT, ‘SEGMENT ID’, and ‘SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?

NO.35 A Data Analyst needs to temporarily hide a tile in a dashboard. The data will need to be available in the future, and additional data may be added. Which tile should be used?

Show/Hide

Duplicate

Delete

Unplace

NO.36 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?

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.

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.

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.

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.

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.

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