Snowflake SnowPro Advanced: Data Engineer (DEA-C02) - DEA-C02 Exam Practice Test

You are using Snowflake Iceberg tables to manage a large dataset stored in AWS S3. Your team needs to perform several operations on this data, including updating existing records, deleting records, and performing time travel queries to analyze data at different points in time. Which of the following statements regarding the capabilities and limitations of Snowflake Iceberg tables are TRUE? (Select all that apply)

Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)

You have implemented a masking policy on the 'EMAIL' column of a 'USERS' table. The policy masks the email address for all users except those with the 'SUPPORT' role. You now need to grant the 'SELECT' privilege on this table to a new role, 'ANALYST. You want to ensure that the masking policy continues to work as expected for the 'ANALYST' role. Which of the following SQL statements should you execute?

You are configuring cross-cloud replication for a Snowflake database named 'SALES DB' from an AWS (us-east-I) account to an Azure (eastus) account. You have already set up the necessary network policies and security integrations. However, replication is failing with the following error: 'Replication of database SALES DB failed due to insufficient privileges on object 'SALES DB.PUBLIC.ORDERS'.' What is the MOST LIKELY cause of this issue, and how would you resolve it? (Assume the replication group and target database exist).

A global e-commerce company, 'GlobalMart', uses Snowflake for its data warehousing needs. They operate primarily in the US (us-east-1) and Europe (eu-west-l). They're implementing cross-region replication for disaster recovery and business continuity. Their requirements are: 1) All data from the US region needs to be replicated to the EU region. 2) The failover to the EU region should have minimal downtime. 3) Replication should be automatic and continuous. Considering these requirements, which of the following Snowflake features and configurations would be the MOST suitable and efficient?

A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

You're working on a data transformation pipeline in Snowflake. You need to create a SQL UDF called that accepts the following parameters: 'price' (NUMBER) (NUMBER) (BOOLEAN) The function should calculate the final price after applying the discount. If the customer , an additional 5% discount should be applied on top of the initial discount. Choose all the valid SQL UDF definitions that accurately implement the requirements. Only one Discount Percentage needs to apply to the base price.

You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?

You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?

You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

You have created a masking policy called which redacts salary information based on the user's role. You have applied this policy to the 'SALARY column in the 'EMPLOYEES table. However, after applying the policy, you notice that even users with the 'ACCOUNTADMIN' role are seeing the masked data, which is not the intended behavior. The intention is that 'ACCOUNTADMIN' and 'SECURITYADMIN' roles should always see the real salary data'. What is the MOST likely cause of this issue and what would you suggest fix that?