DAA-C01 NEW PRACTICE MATERIALS, DAA-C01 100% EXAM COVERAGE

DAA-C01 New Practice Materials, DAA-C01 100% Exam Coverage

DAA-C01 New Practice Materials, DAA-C01 100% Exam Coverage

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
A financial analyst is using Snowflake to forecast stock prices based on historical data'. They have a table named 'STOCK PRICES with columns 'TRADE DATE (DATE) and 'CLOSING PRICE (NUMBER). They want to implement a custom moving average calculation using window functions to smooth out short-term fluctuations and identify trends. Specifically, they need to calculate a 7-day weighted moving average, where the most recent day has the highest weight and the weights decrease linearly. Which SQL statement correctly implements this weighted moving average calculation?

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

Answer: D

Explanation:
Option E is the correct answer because it accurately calculates the 7-day weighted moving average with linearly decreasing weights. It assigns weights from 7 (most recent) down to 1 (oldest) within the 7-day window. The weight calculation '(7 - ROW_NUMBER() OVER (ORDER BY TRADE DATE DESC) + 1)' ensures the most recent date has a weight of 7, and the weights decrease linearly to 1. The sum of the weighted closing prices is then divided by the sum of the weights to get the weighted average. Other options are incorrect because they either calculate a simple moving average, apply incorrect weights, or have syntactic errors. Option B and D's row_number() is ordered ascending, resulting in the oldest data point having the highest weight.


NEW QUESTION # 50
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. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.
  • 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 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.
  • 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. 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.

Answer: A,C

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 # 51
A data analyst is using a data feed from the Snowflake Marketplace which delivers data in JSON format. They need to create a relational table to store this data, but the JSON structure is deeply nested and contains arrays. Which of the following approaches would be the MOST efficient way to create the table and load the JSON data, taking into consideration performance and minimizing data duplication?
(Choose two)

  • A. Create a view on top of the JSON data stored in a single VARIANT column, using JSON path expressions to extract specific fields.
  • B. Use a stored procedure to parse the JSON data and insert it into a pre-defined relational table.
  • C. Create a single table with a VARIANT column to store the entire JSON document.
  • D. Create multiple relational tables, flattening the JSON structure using LATERAL FLATTEN and storing related data in separate tables.
  • E. Load the JSON data into a staging table, then use a series of INSERT statements with JSON parsing functions to populate the final relational table.

Answer: D,E

Explanation:
Options B and E are the most efficient. Option B: Using 'LATERAL FLATTEN' allows you to break down the JSON structure into relational tables, minimizing data duplication and improving query performance for specific fields. Option E: Loading the JSON data into a staging table and then using INSERT statements with JSON parsing functions provides a controlled and efficient way to transform and load the data into the final relational table. Option A: While storing the entire JSON in a VARIANT column is easy, it can lead to performance issues for queries that need to access specific fields within the JSON. Option C: Stored procedures can be complex and less efficient than using native Snowflake JSON parsing functions. Option D: Creating views on VARIANT columns can still suffer from performance issues compared to flattened relational tables.


NEW QUESTION # 52
A data analyst is tasked with loading data into a Snowflake table 'ORDERS' with the following structure: 'CREATE TABLE ORDERS ( ORDER ID INT, CUSTOMER ID INT, ORDER DATE DATE, TOTAL_AMOUNT The data analyst needs to ensure that 'ORDER ID' is unique and not null, 'CUSTOMER ID' references a valid customer in the 'CUSTOMERS' table (column name 'CUSTOMER ID'), and 'ORDER DATE' is not in the future. Which of the following combination of constraints is the most efficient and appropriate way to enforce these Fules in Snowflake? 'CREATE TABLE CUSTOMERS ( CUSTOMER ID INT PRIMARY KEY, CUSTOMER_NAME VARCHAR(255));'.

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

Answer: D

Explanation:
Option E is the most appropriate and efficient. Using a PRIMARY KEY implies an index which while beneficial for joins isn't necessary if simple uniqueness and not null are the primary requirement. A CHECK constraint 'ORDER_DATE <= is the best way to prevent future dates as TRIGGER is not available and view doesn't prevent data from being ingested.


NEW QUESTION # 53
A data analyst is optimizing query performance for a large reporting dashboard that accesses a Snowflake table 'SALES DATA' with millions of rows. The dashboard includes several complex calculations and aggregations based on 'SALE DATE and 'PRODUCT ID' The analyst observes that the dashboard load time is unacceptably slow, even after implementing standard query optimization techniques. Considering Snowflake's caching mechanisms and query profile, which of the following actions would MOST effectively improve the dashboard's performance while minimizing cost?

  • A. Create a materialized view that pre-calculates the aggregations needed by the dashboard. Refresh the materialized view periodically (e.g., daily) to maintain data freshness.
  • B. Increase the virtual warehouse size to a larger configuration (e.g., from X-Small to Large) to ensure sufficient compute resources. This directly speeds up individual query execution.
  • C. Implement query tags and monitor Snowflake query history using the 'QUERY HISTORY view to identify resource-intensive queries and optimize them using query rewriting or indexing techniques.
  • D. Partition the 'SALES_DATX table by 'SALE_DATE to reduce the amount of data scanned during query execution. This avoids unnecessary scans.
  • E. Implement result caching by ensuring that the underlying queries are deterministic and have not been modified. No action is needed; Snowflake automatically manages result caching.

Answer: A

Explanation:
Materialized views offer a significant performance boost by pre-calculating and storing the results of complex aggregations. This reduces the computational load during dashboard refreshes. While increasing virtual warehouse size (A) provides more resources, it's often more cost-effective to optimize queries. Result caching (B) is automatic but depends on query determinism and recent execution. Partitioning (D) is not directly applicable to Snowflake. Query tags and history (E) are helpful for analysis but don't directly speed up dashboard load times.


NEW QUESTION # 54
......

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