DAA-C01 SAMPLE EXAM - DAA-C01 NEW REAL EXAM

DAA-C01 Sample Exam - DAA-C01 New Real Exam

DAA-C01 Sample Exam - DAA-C01 New Real Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q230-Q235):

NEW QUESTION # 230
You're working with a 'WEB EVENTS' table in Snowflake that stores user activity data'. The table includes columns like 'USER ID' , 'EVENT TIMESTAMP', 'EVENT TYPE (e.g., 'page_view', 'button_click', 'form_submission'), and 'EVENT DETAILS' (a VARIANT column containing JSON data specific to each event type). You need to identify users who submitted a specific form ('contact_us') more than 3 times within a 24-hour period. However, you are concerned about data quality, and the 'EVENT TIMESTAMP' column might contain duplicate entries for the same user and event. Which of the following SQL queries is the MOST robust and efficient way to achieve this in Snowflake, ensuring that duplicate timestamps for the same user and 'contact_us' form submission are not counted multiple times?

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

Answer: A

Explanation:
Option B is the most robust and efficient for handling potential duplicate timestamps. Here's why: Inner Query & QUALIFY: The inner query filters for the specific event type ('form_submission') and form ID ('contact_us'). The 'QUALIFY' clause, along with OVER (PARTITION BY USER ID, EVENT TIMESTAMP ORDER BY EVENT TIMESTAMP) = 1', efficiently de-duplicates the data. It assigns a row number to each combination of USER_ID and 'EVENT_TIMESTAMP' , and only keeps the first occurrence (ROW_NUMBER = 1), thus removing duplicate timestamp entries for the same user. Outer Query & Aggregation: The outer query then groups the de-duplicated data by 'USER_ID' and uses 'HAVING COUNT( ) > to identify users who have more than 3 distinct form submissions. Here's why the other options are less suitable: A: This option doesn't handle duplicate timestamps. It will count each duplicate timestamp as a separate form submission, leading to inaccurate results. C: Option C has similar function as option B, however it's slightly less elegant using 'WHERE rn = 1 ' than using Snowflake's Qualify command D: This Option aggregates to just Date Level and thus doesn't provide a robust solution within a 24 hour Period. E: Option E uses a 'COUNT( ) OVER clause that doesn't provide de-duplication of the underlying data, thus a duplicate Event timestamp would be counted twice.


NEW QUESTION # 231
A large retail company is migrating its transaction data to Snowflake and wants to build a consumption layer for BI reporting. They have historical data with frequent updates and require both point-in-time analysis and trend analysis. Which modeling technique(s) would be MOST suitable for this scenario, considering performance, storage efficiency, and the need for both historical tracking and current state views?

  • A. Data Vault modeling, with point-in-time tables built on top of the Data Vault for Bl reporting.
  • B. Snowflake Schema with Slowly Changing Dimension Type 2 (SCD2) for relevant dimensions.
  • C. A wide, denormalized table created using CREATE TABLE AS SELECT (CTAS) statement, refreshed nightly.
  • D. Star Schema with Slowly Changing Dimension Type 1 (SCDI) for all dimensions.
  • E. Star Schema with Slowly Changing Dimension Type 0 (SCDO) for critical dimensions.

Answer: A,B

Explanation:
Data Vault provides the historical tracking and auditability needed, while point-in-time tables on top optimize for BI queries. Snowflake schema with SCD2 also allows historical tracking within a dimensional model. SCDI alone will not preserve history, and SCDO locks in attributes indefinitely. A wide denormalized table is not suitable for historical analysis or frequent updates and can lead to redundancy. Therefore options B and C are suitable for BI reporting.


NEW QUESTION # 232
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?

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

Answer: A

Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.


NEW QUESTION # 233
You are a data analyst at a retail company. You want to enrich your sales data with weather information from the Snowflake Marketplace to analyze the impact of weather conditions on sales. You have a table 'SALES DATA' with columns 'TRANSACTION_DATE (DATE) and 'STORE (INTEGER). You subscribe to a weather data listing from the Snowflake Marketplace that provides weather information by date and location (latitude and longitude). The weather data is in a view called 'WEATHER_DATA' with columns 'DATE' (DATE), 'LATITUDE' (NUMBER), 'LONGITUDE' (NUMBER), and 'TEMPERATURE' (NUMBER). You need to write a SQL query to join these two datasets. However, the 'WEATHER DATA' does not have a 'STORE ID' and requires calculating distance from a known 'STORE LATITUDE' and 'STORE LONGITUDE' stored in a 'STORES' table. Which approach is the MOST efficient and accurate way to enrich 'SALES DATA with 'TEMPERATURE' from 'WEATHER DATA'?

  • A. Create a stored procedure that iterates through each row in 'SALES_DATX , calculates the distance to each weather station in 'WEATHER_DATR , finds the closest weather station, and updates a new 'SALES DATA ENRICHED' table with the temperature. This can be done using the Haversine formula.
  • B. Use a Snowflake UDF (User-Defined Function) that takes 'TRANSACTION_DATE, 'STORE D" , 'STORE_LATITUDE and 'STORE LONGITUDE as input and returns the temperature from the closest weather station in 'WEATHER_DATA' by calculating the Haversine distance within the UDF.
  • C. Join 'SALES_DATX and 'WEATHER_DATX directly on ' TRANSACTION_DATE = 'DATE. Calculate average temperature across all locations for each day to account for location differences. This approach assumes temperature variations are minimal across locations.
  • D. create a new table 'STORE_LOCATIONS' by querying the 'STORES' table that maps 'STORE_ID to 'LATITUDE and 'LONGITUDE. Then, use a CROSS JOIN to create all combinations of 'SALES_DATR, 'STORE_LOCATIONS, and 'WEATHER_DATR and filter based on the proximity (e.g., within 5km) of the store to the weather station using the Haversine formula. Finally, select the closest weather station by using QUALIFY ROW_NUMBER() OVER (PARTITION BY TRANSACTION_DATE, STORE_ID ORDER BY DISTANCE ASC) = 1 .
  • E. Create a view that joins 'SALES DATA' with 'WEATHER DATA' using the 'DATE column. Then, update this view with 'STORE LATITUDE' and ' STORE_LONGITUDE by joining 'SALES_DATA' with the 'STORES' table. Finally, implement a 'CASE statement within the view to calculate the temperature based on the 'LATITUDE and 'LONGITUDE of each store and weather station.

Answer: D

Explanation:
Option C is the most efficient and accurate. Creating a table allows us to pre-calculate store locations. Then, using a 'CROSS JOIN' avoids nested loops, and filtering using the Haversine formula provides accurate proximity-based matching. 'QUALIFY' ensures you select only the closest weather station. Option A is inaccurate as it averages temperatures across all locations. Option B is inefficient due to row-by-row processing within a stored procedure. Option D, while potentially accurate, can suffer from performance issues associated with UDFs, especially when dealing with a large volume of data. Option E is incorrect as you can't update a View directly and the case statement will be difficult to maintain. The Haversine formula calculates the great-circle distance between two points on a sphere given their longitudes and latitudes.


NEW QUESTION # 234
You have a large dataset of sensor readings stored as Parquet files in an external stage. The Parquet files are compressed using Snappy compression. You need to create a Snowflake table that allows you to efficiently query this data, minimizing storage costs and maximizing query performance. Which of the following options represents the MOST efficient approach, considering both storage and query performance, along with any configuration changes needed?

  • A. Create a Snowflake table with a VARIANT column. Copy data into this table from the external stage specifying file format as '(TYPE = PARQUET)'. Create a materialized view based on the data present in the table with variant column.
  • B. Create an external table without specifying any compression options. Snowflake automatically detects the compression. Create a materialized view on top of external stage.
  • C. Create an external table pointing to the Parquet files with 'FILE_FORMAT = (TYPE = PARQUET, COMPRESSION = 'AUTO')'. Snowflake automatically detects the compression. Apply optimize table on the external table.
  • D. Load the Parquet files directly into a Snowflake internal table using 'COPY INTO' without specifying any file format options. Snowflake will automatically handle Snappy compression and optimize for internal storage.
  • E. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly. Specify the 'FILE_FORMAT = (TYPE = PARQUET, COMPRESSION = 'SNAPPY')' option when creating the external stage. Create standard view on top of this external stage. Run analyze table on base table.

Answer: A

Explanation:
Creating an external table and explicitly specifying the file format with 'COMPRESSION = 'SNAPPY" allows Snowflake to efficiently access the data. 'AUTO REFRESH = TRUE' ensures schema evolution is handled, and creating the external table directly allows you to leverage Parquet's columnar storage. 'ANALYZE TABLE allows Snowflake to optimize the query plan. While Snowflake can automatically detect compression in some cases, explicitly specifying it provides more control and can improve performance. 'OPTIMIZE TABLE' is not applicable to external tables. Materialized views on top of External tables is computationally expensive. Using variant column is not optimized approach as it bypasses the benefits of Parquet files.


NEW QUESTION # 235
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