Try and practice the latest Snowflake : DEA-C02 real questions & answers

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

1. You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A) Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'
B) Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.
C) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.
D) create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.
E) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.

2. You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

A) Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE
B) Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.
C) Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
D) Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
E) Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.

3. You have a Snowflake table 'ORDERS with columns 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, and 'TOTAL AMOUNT. You notice that many queries filtering by 'ORDER DATE are slow, even after enabling query acceleration. You decide to implement a caching strategy to improve performance. Which of the following approaches will be most effective in leveraging Snowflake's caching capabilities and improving the performance of date-filtered queries, especially when the data volume for each date is large and varied? Assume virtual warehouse is medium size.

A) Create a clustered table on 'ORDER_DATE. This will physically organize the data on disk, allowing Snowflake to quickly retrieve the relevant data for date- filtered queries.
B) Create a materialized view that pre-aggregates the data by 'ORDER_DATE , such as calculating the sum of 'TOTAL_AMOUNT for each date. This will allow Snowflake to serve the results directly from the materialized view for queries that require aggregation.
C) Use after running a query filtered by 'ORDER_DATE'. This will cache the result of the query in the current session for subsequent queries with the same filter.
D) Increase the data retention period for the 'ORDERS' table. A longer retention period will ensure that more data is available in the Snowflake cache.
E) Apply a WHERE clause with a date range in all the SELECT statements. This forces the metadata caching.

4. A data engineer is responsible for a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a data warehouse. The engineer needs to implement a notification system to alert them when specific data quality issues occur, such as data duplication exceeding a threshold or a sudden drop in data volume. Which approach offers the MOST flexible and scalable solution for implementing these notifications?

A) Create a series of Snowflake Tasks that execute SQL queries to check for data quality issues. If an issue is detected, the task triggers an external function to send a notification to a messaging service (e.g., AWS SNS, Azure Event Grid).
B) Use Snowflake's built-in resource monitors to track data volume and configure alerts based on predefined thresholds. This approach is simple but limited in its ability to detect complex data quality issues.
C) Rely solely on Snowflake's Data Sharing feature to share the data with a data quality team who will manually review the data and report any issues.
D) Implement a data quality monitoring tool that integrates with Snowflake via JDBC/ODBC and uses its own rules engine and notification system to detect and alert on data quality issues.
E) Develop custom SQL scripts to periodically query the data for quality issues and send email notifications using Snowflake's stored procedures and the 'EMAIL' external function.

5. You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A) Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
B) Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
C) Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
D) Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
E) Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.

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