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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q81-Q86):

NEW QUESTION # 81
You are responsible for optimizing query performance on a Snowflake table called 'WEB EVENTS, which contains clickstream data'. The table has the following structure: CREATE TABLE WEB EVENTS ( event_id VARCHAR(36), user_id INT, event_time TIMESTAMP NTZ, event_type VARCHAR(50), page_url VARCHAR(255), device_type VARCHAR(50) Users frequently run queries that filter the 'WEB EVENTS table based on a combination of 'event_type', and a date range derived from 'event_time' You observe that these queries are consistently slow Which of the following strategies would be MOST effective in improving the performance of these frequently executed queries?

A. Create a materialized view that pre-aggregates data by 'event_type' , 'device_type' , and day (derived from 'event_time').
B. Create a clustering key with the following order: 'event_type' , 'device_type' , 'event_time' .
C. Create a search optimization service on the 'page_url' column.
D. Add a column to the 'WEB EVENTS' table for the date part of 'event_time' and create a clustering key using the new date column along with and device_type' .
E. Create a clustering key on 'event_time' .

Answer: A,B

Explanation:
Options C and D are the most effective. A materialized view (C) would pre-compute the frequently requested aggregations, significantly reducing query execution time. Creating a clustering key on 'event_type', and 'event_time' (D) would optimize data organization for the common filter criteria, improving micro-partition pruning. Clustering only on 'event_time' (B) might help with date range filtering but won't address the 'event_type' and 'device_type' filters. Search optimization service (A) is designed for point lookups on string values and is not appropriate for this scenario. Adding a separate column for the date part of the event (E) is redundant and unnecessary. Snowflake can extract date parts from a timestamp column.

NEW QUESTION # 82
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 # 83
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. 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.
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. 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.
D. 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$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
E. Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to

Answer: D

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 # 84
A Snowflake data warehouse contains a table 'WEB EVENTS' with columns like 'EVENT ID', 'EVENT TIMESTAMP, 'USER , 'PAGE URL', and 'SESSION ID'. The data engineering team has enabled search optimization on 'PAGE URL' because analysts frequently filter on specific URLs. However, they notice that queries filtering on multiple 'PAGE URL' values (e.g., using 'WHERE PAGE URL IN ('urll', 'ur12', are not performing as well as expected. What are the potential reasons for this behavior, and what strategies can be used to improve performance in this scenario? Select all that apply:

A. Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
B. The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.
C. Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.
D. Search optimization is not designed to efficiently handle IN list lookups with a large number of values. Consider using a temporary table or common table expression (CTE) to pre-filter the data.
E. The number of distinct values in the 'PAGE URL' column is very high, leading to a large search access path, making IN list lookups inefficient. Consider clustering by PAGE_URL

Answer: D,E

Explanation:
Options A and B are correct. A high cardinality in (Option A) means the search access path is large, making IN list lookups expensive. Search optimization is generally optimized for point lookups or small range scans, not large IN lists. Option B suggests using a temporary table or CTE to pre-filter the data, which can significantly improve performance by reducing the amount of data the optimizer needs to consider during the final filtering step. Option C (increasing warehouse size) might improve performance, but it doesn't directly address the issue of IN list lookups. Option D (analyzing the table) is always a good practice, but it's unlikely to be the primary cause of the slow IN list performance. Option E is wrong, Search optimization is not automatically disabled when using IN clause

NEW QUESTION # 85
You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A. Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
B. Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
C. Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
D. Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
E. Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.

Answer: A

Explanation:
Option D is generally the most efficient. Converting Avro to JSON before ingestion simplifies the integration with Snowpipe and Snowpark. Snowpipe is optimized for semi-structured data like JSON within a VARIANT column. Subsequently, Snowpark DataFrames can easily process the JSON data using built-in functions, avoiding the complexity and potential performance bottlenecks of UDFs (Option B) or custom connectors (Option A). Although Snowflake's function can work with variant data, operating on raw Avro data is not natively supported by Snowpipe without pre-processing or complex UDF logic. External functions (Option E) add another layer of complexity for data retrieval.

NEW QUESTION # 86
......

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Emily Taylor Emily Taylor

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Reliable Snowflake DEA-C02 Braindumps Questions - DEA-C02 Dump Torrent

DEA-C02 Dump Torrent | DEA-C02 Quiz

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q81-Q86):

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