[Jan-2026] Latest Snowflake DAA-C01 exam dumps and online Test Engine
Snowflake DAA-C01: Selling SnowPro Advanced Products and Solutions
NEW QUESTION # 111
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. Operationalizing data complicates dashboard management.
- D. It restricts data updates, affecting dashboard accuracy.
Answer: A
Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.
NEW QUESTION # 112
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?
- A. Stored procedures allow limited data accessibility for improved security.
- B. Stored procedures hinder customization in data operations.
- C. They enable the execution of repetitive tasks, enhancing efficiency.
- D. UDFs enhance query performance more effectively than stored procedures.
Answer: C
Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.
NEW QUESTION # 113
How do materialized views differ from secure views in data analysis?
- A. Secure views provide precomputed snapshots, unlike materialized views.
- B. Materialized views offer enhanced data security while allowing selective data access.
- C. Secure views precompute data, unlike materialized views.
- D. Materialized views restrict data access for improved security.
Answer: C
Explanation:
Secure views offer enhanced data security without precomputing data, distinguishing them from materialized views.
NEW QUESTION # 114
You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?
- A. Create a stored procedure that iterates through each transaction in 'TRANSACTIONS' , attempts to find a matching economic data record in ECONOMIC_DATA' based on date and zip code, and updates a new 'TRANSACTIONS_ENRICHED table with the economic indicators. Handles missing zipcodes by setting the 'UNEMPLOYMENT RATE' and 'GDP ' to 0 for any record in transaction which zip code is missing.
- B. Create a view that first filters out all rows with missing or malformed zip codes from both 'TRANSACTIONS' and 'ECONOMIC DATA' using 'WHERE clauses and regular expressions to validate the zip code format. Then, perform an ' INNER JOIN' between the filtered datasets on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE.
- C. Create a Snowflake Task that runs daily to update a materialized view that joins 'TRANSACTIONS' and 'ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE , handling missing zip codes by skipping those records entirely.
- D. Create a view that performs a simple 'JOIN' between 'TRANSACTIONS' and 'ECONOMIC DATA' on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC_DATDATE and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE. This approach ignores missing or malformed zip codes.
- E. Create a view using a 'LEFT OUTER JOIN' between 'TRANSACTIONS and ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION_DATE =ECONOMIC_DATA.DATE' and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE'. Additionally, use the function to handle malformed zip codes and the 'NVL' function to replace missing or malformed zip codes with a default zip code (e.g., '00000') for joining purposes. Also include a new column "ENRICHMENT SUCCESS' that flag indicates that the join was successful or whether data was enriched using the default zip code.
Answer: E
Explanation:
Option C provides the most robust and efficient solution. Using 'LEFT OUTER JOIN' ensures that all transactions are included in the view, even if there is no matching economic data. 'TRY TO NUMBER handles malformed zip codes gracefully by converting valid zip codes to numbers and returning NULL for invalid ones, preventing errors. 'NVL' replaces NULL zip codes (either originally missing or resulting from TRY_TO_NUMBER) with a default value, allowing the join to proceed using a fallback. Adding the 'ENRICHMENT_SUCCESS' flag provides transparency about which records were enriched using the default zip code, enabling users to assess the reliability of the enriched data. Option A is inadequate because it ignores missing or malformed zip codes, leading to data loss. Option B is inefficient and not scalable due to row-by-row processing. Option D discards records with missing or malformed zip codes, resulting in significant data loss. Option E does not specifically handle data quality issues related to missing or malformed zip codes. Further the use of Tasks and materialized views, while increasing performance, doesn't necessarily address the issue of data quality.
NEW QUESTION # 115
A data analyst is working with JSON data representing product reviews. The JSON structure is complex, containing nested arrays and objects. The analyst needs to extract all the reviewer names (reviewer _ name) who gave a rating greater than 4, along with the product ID (product_id) and the overall average rating of that specific product. The table 'RAW REVIEWS contains a single VARIANT column named holding the JSON data'. Choose the most efficient and correct Snowflake SQL query to achieve this. Assume the JSON structure is consistent across all rows.
- A. Option A
- B. Option D
- C. Option C
- D. Option B
- E. Option E
Answer: C
Explanation:
Option C correctly uses LATERAL FLATTEN to unnest the 'reviews' array within the JSON. It accurately extracts 'reviewer_name' and 'rating' from the flattened array 'r'. Critically, it correctly retrieves directly from the 'raw_json' column as the product ID is not within the nested 'reviews' array, but at the root level. The average rating is correctly calculated using a window function partitioned by the Option A incorrectly references the product_id in the flatten table.Option B is inefficient and incorrect because it assumes only one review exists ([0]) and uses GET PATH, which is less performant than direct path access. Options D is also incorrect for average rating as raw_json:reviews[0].rating will pick only the first rating in JSON to derive avg_rating.
NEW QUESTION # 116
What distinguishes exploratory ad-hoc analyses from routine data analysis?
- A. Ad-hoc analyses focus only on established trends.
- B. They query known patterns without further exploration.
- C. Ad-hoc analyses rely solely on predefined queries.
- D. Ad-hoc analyses explore patterns and anomalies beyond established routines.
Answer: D
Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.
NEW QUESTION # 117
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: D
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 # 118
Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply
- A.
- B.
- C.
- D.
- E.
Answer: B,E
Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR
NEW QUESTION # 119
What types of Snowflake functions are available for data analysis and manipulation? (Select all that apply)
- A. Scalar functions
- B. Aggregate functions
- C. System functions
- D. Complex functions
Answer: A,B,C
Explanation:
Snowflake functions include scalar, aggregate, and system functions for data analysis and manipulation.
NEW QUESTION # 120
You're developing a data quality process in Snowflake that relies on identifying duplicate records within a large table named 'TRANSACTIONS. You need to generate a hash value for each row based on several key columns ('transaction_id', 'customer_id' , amount', to efficiently compare rows and detect duplicates. However, some of these columns may contain NULL values, which you want to handle consistently during the hash generation. Which of the following approaches, utilizing Snowflake's system functions, will MOST reliably generate a consistent hash value for duplicate rows, even when some of the key columns contain NULLs? (Select TWO)
- A. Use the customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
- B. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.
- C. Use the AS VARCHAR), AS VARCHAR), AS VARCHAR), NVL(CAST(transaction_date AS VARCHAR), function, explicitly converting each value to a string and replacing NULLs with empty strings using NVL.
- D. Use the WS('I', transaction_id, customer_id, amount, function. This concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies.
- E. Use the 'SHA2(CONCAT(NVL(transaction_id, 'I), NVL(customer_id, "), NVL(amount, "), NVL(transaction_date, "))) function, replacing NULLs with empty strings using the NVL function before concatenation.
Answer: C,E
Explanation:
Options B and E are the most reliable. Option B concatenates the value of the columns as a string to create a seed for SHA2, ensuring to convert the NULL to empty string, which is necessary so that SHA2 does not return NULL in the face of NULL column values. Option E also uses SHA2 to encrypt after concatenating all the column values but it casts all those columns to varchar, which is necessary for the data preparation and data ingestion as they might be of different datatype. The first option is wrong because Snowflake's HASH function automatically returns NULL if any of the input are NULL. Option C uses the 'II' operator to concatenate values and Snowflake will return NULL in case any value is null. Option D concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies
NEW QUESTION # 121
You are troubleshooting a slow-running query that performs a complex transformation on a large dataset using a series of CTEs. You've identified one particular CTE, , as contributing significantly to the overall execution time. Examination of the query profile reveals high spillover to local storage during the execution of this CTE. Which of the following actions could potentially reduce spillover and improve the performance of the CTE? Select all that apply.
- A. Reduce the 'MAX MEMORY USAGE IN parameter at the session level.
- B. Create a materialized view from the 'calculate metrics CTE.
- C. Increase the warehouse size.
- D. Partition the underlying tables used in the CTE.
- E. Rewrite the CTE to use smaller intermediate tables.
Answer: C,E
Explanation:
Increasing the warehouse size (A) provides more memory, which directly reduces spillover. Rewriting the CTE to use smaller intermediate tables (C) reduces the memory footprint of the operation. Partitioning the tables (B) is not directly supported in Snowflake. Creating a materialized view from the CTE (D) can improve performance if the CTE's results are frequently reused, but it does not necessarily address the spillover issue directly. Reducing the 'MAX (E) would worsen spillover. Using temporary tables (E) will not necessarily impact spilling if the memory limits are still exceeded within the query runtime.
NEW QUESTION # 122
How do Stored Procedures contribute to the efficiency of data analysis using SQL?
- A. They solely facilitate basic arithmetic operations.
- B. They limit data accessibility, hindering analysis.
- C. Stored Procedures enable the execution of repetitive tasks, enhancing efficiency.
- D. Stored Procedures can't be used in conjunction with User-Defined Functions (UDFs).
Answer: C
Explanation:
Stored Procedures aid in data analysis by enabling the execution of repetitive tasks, thereby enhancing efficiency.
NEW QUESTION # 123
When managing Snowsight dashboards, what significance do subscriptions and updates hold in meeting business requirements?
- A. Managing subscriptions and updates complicates dashboard usage.
- B. Subscriptions and updates have no impact on dashboard management.
- C. They enhance dashboard usability without data updates.
- D. Subscriptions and updates ensure timely information delivery.
Answer: D
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 124
You are tasked with loading JSON files into Snowflake using Snowsight. The JSON files are semi-structured and contain nested arrays and objects. You want to flatten the JSON structure during the load process to facilitate easier querying. Which of the following Snowsight-integrated features or approaches are suitable for flattening the JSON data during the load process? Select all that apply.
- A. Use Snowsight's 'Load Data' wizard to load the JSON files directly into relational tables by defining mappings between JSON elements and table columns. Snowsight automatically flattens the JSON structure based on the defined mappings.
- B. Load the JSON data into a staging table as raw JSON strings. Then, write a custom Python User-Defined Function (UDF) that parses the JSON strings and returns a flattened representation of the data. Use this UDF in a 'CREATE TABLE AS SELECT statement to load the flattened data into a new table.
- C. Use Snowsight's 'Load Data' wizard along with Snowflake's built-in functions like 'GET_PATH' and operator within the computed column expressions during the load process to extract and load specific elements from the JSON data into individual columns.
- D. Create a Snowpipe that automatically loads new JSON files from a stage into a Snowflake table. Within the Snowpipe definition, define a transformation that uses 'LATERAL FLATTEN' to flatten the JSON data before loading it into the target table.
- E. Load the JSON data into a VARIANT column in Snowflake. Then, use SQL with 'LATERAL FLATTEN' to query and extract data from the nested structures, creating a view to represent the flattened data.
Answer: C,D,E
Explanation:
Options A, C, and E are all suitable approaches for flattening JSON data. Option A leverages 'LATERAL FLATTEN' in SQL, which is a common and efficient way to flatten JSON structures after loading into a VARIANT column. Option C utilizes Snowpipe with a transformation including 'LATERAL FLATTEN' , enabling automatic flattening during ingestion. Option E uses computed columns in Snowsight to extract JSON elements during loading using 'GET_PATH' or the operator. Option B is incorrect; Snowsight's 'Load Data' wizard doesn't automatically flatten JSON during the initial load. It mainly handles the basic load of the JSON file into a VARIANT column, not automatic flattening. Option D is a valid but less performant alternative; UDFs can be slower than native Snowflake SQL functions for large datasets.
NEW QUESTION # 125
You are building a real-time data pipeline to ingest IoT sensor data into Snowflake. The data arrives in Avro format via Kafka topics. You need to ensure data integrity, minimize latency, and accurately determine the data volume ingested for billing and monitoring purposes. Which of the following options provide the BEST combination of techniques to achieve these goals? (Select TWO)
- A. Utilize Snowflake Streams and Tasks to create a change data capture (CDC) pipeline within Snowflake. Initially, load all data into a raw table. Then, use a stream to track changes, validate the data, and apply transformations incrementally.
- B. Use Snowflake's Kafka connector to directly load data from Kafka into a raw data table. After loading, run scheduled tasks to perform data quality checks and transformations.
- C. Implement a custom Kafka consumer application that validates and transforms the Avro data before loading it into a staging table in Snowflake using the Snowflake Python connector. Use a Snowflake Task to move data from the staging to the final table.
- D. Implement a stream processing framework (e.g., Apache Flink, Spark Streaming) to consume data from Kafka, perform data quality checks and transformations, and then load the processed data into Snowflake using the Snowflake JDBC driver.
- E. Use Snowpipe with auto-ingest to continuously load data from a cloud storage location (e.g., AWS S3, Azure Blob Storage) where Kafka Connect is writing the Avro data. Configure Snowpipe error notifications to capture data quality issues. Do not perform Transformation.
Answer: A,D
Explanation:
Options B and E provide the best combination. Option B uses a stream processing framework for real-time validation and transformation before loading into Snowflake, minimizing latency and ensuring data quality. Option E leverages Snowflake Streams and Tasks for CDC, enabling incremental data validation and transformation within Snowflake, ensuring data integrity and volume tracking.
NEW QUESTION # 126
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?
- A. Database containing HR employee data.
- B. Salesforce data containing customer interactions and sales opportunities.
- C. Website clickstream data stored in AWS S3 buckets in Parquet format.
- D. Social media activity data ingested via a third-party API and stored in a relational database.
- E. IoT sensor data containing temperature readings.
Answer: B,C,D
Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.
NEW QUESTION # 127
You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?
- A. Create a standard view using a JOIN between 'CUSTOMER DATA' and 'COUNTRY INFORMATION'. Refresh the view regularly using a scheduled task.
- B. Create a secure view joining the two tables and granting access to users.
- C. creates a materialized view with clustering enabled on the 'COUNTRY_CODE column after joining 'CUSTOMER_DATX and 'COUNTRY_INFORMATION'.
- D. Create a materialized view using a simple JOIN between 'CUSTOMER_DATA' and 'COUNTRY_INFORMATION'.
- E. Use a User-Defined Function (UDF) to look up the currency code from the "COUNTRY_INFORMATION' table based on the customer's country code.
Answer: C
Explanation:
Materialized views generally provide better performance than standard views for complex queries, especially with joins. Clustering on the 'COUNTRY_CODE column further enhances performance by physically organizing the data based on this column, making lookups more efficient. Using secure view wouldn't impact on performance. Regular view refreshment using scheduled task is not as efficient compared to materialized view. UDFs can introduce performance overhead, especially for large datasets.
NEW QUESTION # 128
Why are materialized views valuable in data analysis compared to regular views?
- A. Regular views simplify complex data structures for ease of analysis.
- B. Materialized views restrict data access, enhancing security.
- C. Materialized views provide precomputed snapshots, improving query performance.
- D. They offer real-time updates reflecting instantaneous database changes.
Answer: C
Explanation:
Materialized views provide precomputed snapshots, improving query performance.
NEW QUESTION # 129
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?
- A. Primary keys allow only one-to-one table joins
- B. Constraints restrict data access to authorized user roles
- C. Constraints limit table join operations to specific data types
- D. Primary keys ensure data uniqueness for table relationships
Answer: D
Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.
NEW QUESTION # 130
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?
- A. Option C
- B. Option A
- C. Option B
- D. Option E
- E. Option D
Answer: B,E
Explanation:
Options A and D are correct. Option A uses a window function OVER (PARTITION BY to calculate the total sales for each day. This is efficient because it calculates the total sales for each day in a single pass. Option D uses a subquery to calculate the total sales for each day and then joins this result back to the original table. Option B calculates a single total over the entire table, not by day. Option C calculates a single total sales amount over the entire table and assigns it to every row, which is incorrect. Option E orders the sales amount by the date but doesnt correctly partition by 'sale_date' leading to cumulative sum instead.
NEW QUESTION # 131
When employing different data models (e.g., dimensional, Data Vault) in Snowflake, how does Data Vault modeling address complexities in data integration and changes?
- A. Data Vault models restrict data access to specific user roles
- B. Data Vault models simplify data integration and adapt well to changes
- C. Data Vault models enhance data transformation capabilities
- D. Data Vault models offer limited scalability and flexibility
Answer: B
Explanation:
Data Vault models simplify data integration and adapt well to changes, providing advantages in handling complexities and evolutions in data structures.
NEW QUESTION # 132
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