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ValidBraindumps DEA-C02 Desktop Practice Exams

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q31-Q36):

NEW QUESTION # 31
A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

A. Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
B. Use a 'LATERAL FLATTEN' function to process the data in parallel.
C. Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering
D. Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.
E. Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.

Answer: A

Explanation:
Since the 'USER_DEVICES' table is small, broadcasting it to all compute nodes allows Snowflake to perform a local join, avoiding network transfers and significantly improving performance. Using the 'BROADCAST hint will make use of this functionality. Standard join will not be efficient. 'LATERAL FLATTEN' is for semi-structured data. While Materialized views improves performance, here Broadcasting is the most cost effective. Using VARIANT data type and JSON path expression slows down the query.

NEW QUESTION # 32
You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

Answer: A

Explanation:
The correct syntax to apply a masking policy to a column in Snowflake is SALTER TABLE ALTER COLUMN SET MASKING POLICY ' . Therefore, option B is the correct answer.

NEW QUESTION # 33
You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A. Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
B. Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
C. Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
D. Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
E. Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

Answer: B

Explanation:
Using Streams and Tasks for incremental updates (option B) is the most efficient and low-latency solution. It minimizes data processing time and cost compared to full refreshes (options A and C). Manual uploads (option D) are not automated. Sharing raw data files (option E) puts the burden of data processing on the consumer, which is less desirable for a data product.

NEW QUESTION # 34
You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

A. Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the 'SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use 'WHEN SYSTEM$STREAM HAS to run the Task.
B. Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use 'VALIDATE ( STAGE NAME '0'.////' before COPY INTO.
C. Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use 'ON ERROR = CONTINUE' during COPY INTO.
D. Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to
E. Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'

Answer: E

Explanation:
Snowpipe is specifically designed for continuous data ingestion with minimal latency. It leverages event notifications and serverless compute resources, making it more efficient than polling-based approaches (Task + Stream) or Lambda function invocations. The use of 'SYSTEM$PIPE STATUS' for monitoring and 'ALTER PIPE ... REFRESH' for manual retries provides better control and error handling compared to manual COPY INTO commands and MERGE statements. Option A is inefficient, B is complex, C might have performance issues due to high concurrency and E requires more coding and Stream-related management.

NEW QUESTION # 35
You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

A. Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.
B. Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
C. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
D. Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.
E. Utilize a Java UDF to parse the string and directly generate the JSON array.

Answer: C,D

Explanation:
Options A and D correctly outline the process. (A) and multiple 'SPLIT calls (D) are valid approaches to break down the concatenated string. Then, 'OBJECT_CONSTRUCT builds the individual JSON objects, and aggregates them into a JSON array. While Javascript or Java UDFs (C, E) could solve the problem, they are generally less efficient than Snowflake's built-in functions. (B) might work but is overkill for this simple splitting task, also you would still need to combine the extracted arrays for items and quantities.

NEW QUESTION # 36
......

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