최신 DAA-C01 무료덤프 - Snowflake SnowPro Advanced: Data Analyst Certification

문제1

You are designing a system to ingest data from a high-volume sensor network. The sensors send data in a custom binary format to an on-premise message queue (e.g., RabbitMQ). The data needs to be converted to a structured format (e.g., JSON) before being loaded into Snowflake. Choose the most effective approach to ensure data integrity, scalability, and near-real-time ingestion.

A. Develop a custom application that subscribes to the message queue, converts the binary data to JSON, and then uses the Snowflake JDBC driver to insert the data directly into Snowflake.

B. Deploy a stream processing engine (e.g., Apache Kafka Streams, Apache Flink) on-premise to consume messages from the queue, convert the binary data to JSON, and then write the JSON data to a cloud storage location (e.g., S3, Azure Blob Storage, GCS). Configure Snowpipe to load the JSON data into Snowflake.

C. Use an on-premise gateway to expose the RabbitMQ as a REST API, then use a Snowflake external function to call the exposed API.

D. Create a Snowflake external function that connects to the message queue and converts the binary data to JSON during the COPY INTO process.

E. Use a third-party data integration platform that supports connecting to message queues, converting binary data, and loading data into Snowflake.

정답: B,E

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문제2

You are tasked with aggregating website clickstream data in Snowflake to identify the most popular product categories per region on a daily basis. The clickstream data is stored in a table named 'clickstream eventS with columns: 'event_time', 'user id', 'product id', 'region', and 'category'. You need to create a solution that efficiently identifies the top 3 categories for each region on each day. Which approach offers the best performance and scalability considering the dataset size is expected to grow significantly?

A. Using a simple GROUP BY operation to count the occurrences of each category, region, and day, then relying on external tools to filter the top 3.

B. Creating a series of temporary tables for each region, aggregating the data, and then using a JOIN operation to combine the results.

C. Using a combination of GROUP BY, RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3.

D. Implementing a stored procedure that iterates through each region and day, calculating the category counts and selecting the top 3 using procedural logic.

E. Using a combination of GROUP BY, DENSE_RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3. DENSE RANK handles ties more gracefully.

정답: E

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문제3

You are tasked with building a report to analyze customer churn. The data includes customer demographics, purchase history, website activity, and support interactions. You want to provide interactive filtering capabilities within the report, allowing users to explore the data based on various criteria'. Which Snowflake feature(s) offer the most effective way to implement interactive filtering within your reporting solution without directly exposing underlying tables?

A. Using Snowflake's Data Marketplace to directly share the raw tables with the reporting tool.

B. Using Snowflake's external functions to call a custom API that handles filtering logic.

C. Creating secure views and stored procedures, then implementing parameterized queries within the reporting tool that call the stored procedures with user-defined parameters.

D. Granting direct read access to all the underlying tables to the reporting tool's service account.

E. Creating static reports using a scheduling tool to generate PDFs.

정답: C

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문제4

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 E

B. Option D

C. Option C

D. Option B

E. Option A

정답: D

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문제5

Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.

B. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.

C. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.

D. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.

E. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.

정답: B

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문제6

You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?

A. Create a materialized view partitioned by and clustered by 'event_type' , pre-extracting relevant fields from 'event_detailS into separate columns.

B. Add a masking policy on the 'event_details' column and recluster the table by 'user_id'.

C. Create a view that extracts specific fields from the 'event_details' column into separate columns and add a secondary index on 'event_type' .

D. Change the partition key to 'event_type' and create a table function to query sevent_detailss.

E. Create a temporary table containing the results and then performing a Merge operation.

정답: A

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문제7

You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

A. Option E

B. Option D

C. Option C

D. Option B

E. Option A

정답: C

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문제8

Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

A. Create a temporary table to store the total revenue for each product, then select the top 10 from the temporary table. Join this result with 'sales_data' and apply ' SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey for the cumulative revenue calculation.

B. Use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey to calculate the cumulative revenue for all products, then filter the results to include only the top 10 products based on their final cumulative revenue.

C. Use a subquery to find the top 10 'product_id' based on total revenue, then join this subquery with 'sales_data' and use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)' to calculate the cumulative revenue.

D. Use the aggregation to identify top 10 'product_id' and calculate the cumilative revenue using SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)'.

E. Use a window function to rank products by total revenue, then filter for the top 10 ranks using a 'QUALIFY' clause. Finally, calculate cumulative revenue using 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey.

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제9

You're using Snowsight to build a dashboard for monitoring website performance. The data is in a table called 'WEB EVENTS' with columns: 'EVENT _ TIME' (TIMESTAMP_NTZ), 'EVENT _ TYPE' (VARCHAR, e.g., 'page_view', 'button_click'), 'USER_ID' (VARCHAR), and 'PAGE URL' (VARCHAR). You want to create a tile that shows the average time between consecutive 'page_view' events for each user over the last 7 days. This will help you understand how users are navigating the site. Assume that for a single user, page_view events are ordered by EVENT TIME. Which of the following SQL queries, when used as the basis for a Snowsight tile, will correctly calculate this average time difference in seconds?

A. Option E

B. Option D

C. Option C

D. Option B

E. Option A

정답: E

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문제10

A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

A. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.

B. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.

C. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.

D. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.

E. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.

정답: E

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문제11

A marketing team needs a daily report showing the conversion rate of leads to customers. They define conversion rate as (Number of Customers Acquired / Total Number of Leads) 100. The data resides in two tables: 'LEADS' and 'CUSTOMERS'. 'LEADS' contains all leads generated daily, and 'CUSTOMERS' contains all acquired customers, both tables having a 'LEAD_ID' and 'ACQUISITION DATE' field, with 'ACQUISITION DATE' being NULL in the 'LEADS' table. They want the report automated and delivered via email. Which combination of Snowflake features would BEST accomplish this task?

A. Create a stream on both 'LEADS' and 'CUSTOMERS' tables. Use a task to monitor the stream and calculate the conversion rate, then email the results.

B. Create a Python UDF to calculate the conversion rate and create a scheduled task using it. Use Snowpark DataFrame APIs to read from and join the 'LEADS' and 'CUSTOMERS' tables, then use Snowflake email integration to deliver the report.

C. Create a dashboard using Snowsight connected to the 'LEADS' and 'CUSTOMERS tables. Schedule a daily email snapshot of the dashboard.

D. Create a stored procedure that calculates the conversion rate, then schedule a Snowflake task to execute the stored procedure daily. Use Snowflake's email integration to send the results.

E. Create a view that calculates the conversion rate. Use a third-party ETL tool to extract data from the view daily and send an email report.

정답: D

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문제12

You observe a 'Spilling to Local Storage' event in the Query Profile of a complex aggregation query. This indicates that the intermediate results are exceeding the memory capacity of the virtual warehouse. Which of the following actions would be MOST effective in mitigating this issue and improving the query performance?

A. Reduce the number of distinct values in the grouping columns.

B. Increase the virtual warehouse size to provide more memory resources.

C. Implement result caching to avoid re-computation.

D. Optimize the query to reduce the amount of data processed before the aggregation.

E. Increase the value of the 'MAX MEMORY USAGE parameter for the session.

정답: A,B,D

설명: (DumpTOP 회원만 볼 수 있음)

최신 DAA-C01 무료덤프 - Snowflake SnowPro Advanced: Data Analyst Certification

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