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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q97-Q102):

NEW QUESTION # 97
You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

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

Answer: B

Explanation:
Option D is the most efficient and correct. It uses 'snowflake.snowpark.functions' to correctly reference the columns using and 'dateadd()" for date arithmetic. Option A and C attempts to use native python date functions, and Option E passes a SQL string directly to the filter, bypassing Snowpack's function calls. and 'filter()' are functionally equivalent in Snowpark. Option B, while technically correct, uses 'dateadd' with which is better suited for Snowflake SQL than Snowpark operations. Option D keeps the entire filtering logic within Snowpark.

NEW QUESTION # 98
You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

A. Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.
B. Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
C. When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.
D. Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
E. Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.

Answer: C

Explanation:
The best solution for idempotent processing is option C, using a MERGE statement with a staging table and timestamp-based logic. This allows you to load all changes into a staging area, then merge only the latest version (based on 'timestamp' ) into the target table, effectively overwriting older versions. Option A, using a regular task and MERGE, can work, but it doesn't inherently address idempotency without the staging table and timestamp logic. Option B, using a materialized view, can work but does not capture all changes from the original table. Option D can work, but it doesnt scale well. Option E is incorrect; streams do not support unique key constraints to prevent capturing duplicate updates.

NEW QUESTION # 99
A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A. Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
B. Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.
C. Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.
D. Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.
E. Recluster the entire table in a single transaction during off-peak hours.

Answer: B

Explanation:
Snowflake's automatic reclustering feature is designed specifically to address this scenario. It automatically reclusters data in the background, minimizing the impact on concurrent DML operations. Options A and E will lock the table and can cause performance impacts during peak hours. Option B is more complex and requires significant downtime during the swap. Option C describes Snowpipe data ingestion, which isn't reclustering. While creating clustered tables with Snowpipe is possible, this option does not address the already unclustered data. Adjusting the warehouse size is relevant to reclustering performance but is secondary to enabling the automatic reclustering itself.

NEW QUESTION # 100
You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A. Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
B. Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
C. Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
D. Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
E. Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.

Answer: B,C,E

Explanation:
Options A, B, and D are the most effective. Option A provides a programmatic approach within Snowflake. Option B leverages Snowflake's built-in data quality capabilities (if available, check documentation for supported features and editions). Option D integrates with external specialized tools. Option C relies on manual monitoring, which is less proactive. Option E does not address the root cause of data quality issues and could potentially overwrite valid data with erroneous data.

NEW QUESTION # 101
You have a Snowflake table 'ORDERS' with billions of rows storing order information. The table includes columns like 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, 'PRODUCT_ID', and 'ORDER AMOUNT'. Analysts frequently run queries filtering by 'ORDER DATE' and 'CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you've already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

A. Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.
B. Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
C. Enable search optimization on the 'ORDER_DATE' column.
D. Enable search optimization on the 'PRODUCT ID column.
E. Enable search optimization on the 'ORDER_ID column.

Answer: A

Explanation:
Enabling search optimization on both 'ORDER_DATE and will directly benefit queries filtering by these columns. Search optimization is designed to significantly speed up point lookups and range scans. A materialized view (option D) might help, but it introduces the overhead of maintaining the view and might not be as flexible as search optimization for ad-hoc queries. Options A and E are incorrect since they focus on columns not frequently used in the specified filtering criteria.

NEW QUESTION # 102
......

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