CERTIFICATION DAA-C01 EXAM INFOR | DAA-C01 VALID EXAM CAMP PDF

Certification DAA-C01 Exam Infor | DAA-C01 Valid Exam Camp Pdf

Certification DAA-C01 Exam Infor | DAA-C01 Valid Exam Camp Pdf

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q257-Q262):

NEW QUESTION # 257
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

  • A. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
  • B. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
  • C. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
  • D. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;
  • E. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;

Answer: D

Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.


NEW QUESTION # 258
You are tasked with optimizing a dashboard displaying sales performance across different regions. The dashboard queries a large table called 'SALES DATA, which is partitioned by 'SALE DATE. The dashboard loads very slowly. The data analyst team notices that queries for the dashboard often filter on a specific region and a date range. Which of the following steps can you take to improve the dashboard's performance? (Select TWO)

  • A. Create a search optimization policy on the 'SALES DATA' table for the 'REGION' column.
  • B. Recluster the SALES DATA' table on 'REGION' and 'SALE DATE.
  • C. Increase the size of the warehouse used to query the 'SALES DATA' table.
  • D. Create a table clone of 'SALES_DATX and point the dashboard to the clone instead.
  • E. Create a materialized view that pre-aggregates the data by region and date. The materialized view should be incrementally updated.

Answer: B,E

Explanation:
Creating a materialized view (A) pre-aggregates the data, significantly reducing the amount of data that needs to be processed for each dashboard refresh. Reclustering on 'REGIONS and (D) physically organizes the data on disk based on these frequently used filter columns, improving query performance by minimizing the need to scan irrelevant partitions. Table clones (B) provide a separate copy of the data but don't inherently improve query performance. Search optimization (C) is useful for point lookups, but not as effective for date range filtering in this scenario. Increasing warehouse size (E) helps, but optimizing the data structure offers a more fundamental and cost-effective improvement.


NEW QUESTION # 259
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?

  • A. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.
  • B. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.
  • C. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
  • D. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
  • E. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.

Answer: A,B

Explanation:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.


NEW QUESTION # 260
A data analyst is tasked with creating a near-real-time dashboard using Streamlit and Snowflake to monitor website traffic. The website traffic data is continuously ingested into a Snowflake table named 'WEB TRAFFIC EVENTS' with columns 'EVENT TIME' (TIMESTAMP LTZ), 'PAGE URL' (VARCHAR), and 'USER ID' (VARCHAR). The analyst wants to ensure the Streamlit dashboard automatically reflects the latest data in Snowflake with minimal latency. Which of the following approaches would provide the MOST efficient and near-real-time data updates in the Streamlit dashboard?

  • A. Configure Streamlit to use the Snowflake Connector for Python with auto-commit enabled. This ensures that any changes made to the data in Streamlit are immediately reflected in Snowflake.
  • B. Leverage Snowflake's Change Data Capture (CDC) capabilities to track changes in the ' table. Streamlit can then query the CDC stream to retrieve only the changes since the last refresh, minimizing the amount of data transferred and processed. Utilize 'st.cache_data' with a short TTL.
  • C. Use Streamlit's 'st.cache_data' decorator with a long TTL (time-to-live) on a function that queries Snowflake directly for each dashboard refresh. This will cache the query results for a longer period, reducing the load on Snowflake.
  • D. Use Streamlit's to directly edit data in the Snowflake table from the dashboard. This provides real-time updates by allowing users to modify the data directly.
  • E. Implement Snowflake's Snowpipe to continuously load data into a separate summary table containing pre-aggregated metrics (e.g., page views per minute). Streamlit can then query this summary table frequently without impacting Snowflake's performance. Use 'st.cache_data' with a short TTL to refresh the data quickly.

Answer: B

Explanation:
Leveraging Snowflake's CDC capabilities is the most efficient way to achieve near-real-time data updates in Streamlit. CDC allows you to retrieve only the changes made to the table since the last refresh, significantly reducing the amount of data transferred and processed compared to querying the entire table each time. Snowpipe is a good option, but is more oriented to data ingestion and not data changes. Option A caches results, which is opposite of real time. Option B is useful, but more complex than needed. Option C is not related to the problem. Option D is incorrect because 'auto-commit' applies only to data updates from the application to Snowflake. Streamlit can then query this CDC stream to get an updated set of data. Using 'st.cache_data' with a short TTL ensures the dashboard reflects recent data changes from the CDC Stream.


NEW QUESTION # 261
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?

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

Answer: A,E

Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.


NEW QUESTION # 262
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