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

1. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

A) Option B
B) Option D
C) Option E
D) Option C
E) Option A

2. You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

A) Option B
B) Option D
C) Option E
D) Option C
E) Option A

3. A data engineering team is running a series of complex analytical queries against a large Snowflake table. They notice that query performance is inconsistent, with some queries running much slower than others. After investigation, they determine that the queries are not properly leveraging the data clustering. Which of the following actions could improve the query performance related to the data clustering? Select all that apply.

A) Increase the virtual warehouse size to improve the performance of all queries, regardless of clustering.
B) Run 'SHOW TABLES LIKE ' ' and review the 'clustering_information' column to understand the current clustering depth and benefit.
C) Execute SYSTEM$CLUSTERING INFORMATION( ' )' y to assess the impact of clustering on query performance and determine if re-clustering is needed.
D) Run 'ALTER TABLE CLUSTER BY (columnl, column2Y to explicitly define a clustering key on the table.
E) Run 'ALTER TABLE DROP CLUSTERING KEY to remove the existing clustering key and allow Snowflake to automatically cluster the data.

4. Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

A) Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.
B) Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.
C) Create a replication group in the source AWS account and add the databases and shared objects to it.
D) Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
E) Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.

5. A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A) Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
B) Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
C) Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
D) Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
E) Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.

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