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[General] Most Probable Real Exam Questions in Snowflake DEA-C02 PDF Dumps Format

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【General】 Most Probable Real Exam Questions in Snowflake DEA-C02 PDF Dumps Format

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q340-Q345):NEW QUESTION # 340
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. The network policy is blocking access to the ORDERS table. Update the network policy to allow access to the ORDERS table.
  • B. The target Azure account does not have sufficient storage capacity. Increase the storage quota for the Azure account.
  • C. The replication group is missing the 'ORDERS' table. Alter the replication group to include the 'ORDERS' table: 'ALTER REPLICATION GROUP ADD DATABASE SALES DB;'
  • D. The user account performing the replication does not have the 'ACCOUNTADMIN' role in the AWS account. Grant the 'ACCOUNTADMIN' role to the user.
  • E. The replication group does not have the necessary permissions to access the 'ORDERS' table in the AWS account. Grant the 'OWNERSHIP' privilege on the 'ORDERS table to the replication group: 'GRANT OWNERSHIP ON TABLE SALES DB.PUBLIC.ORDERS TO REPLICATION GROUP
Answer: E
Explanation:
The error message indicates a privilege issue on the 'ORDERS table. Replication requires the replication group to have 'OWNERSHIP' privileges on the objects being replicated. Granting 'OWNERSHIP ensures that the replication process can access and replicate the table's data and metadata. ACCOUNTADMIN is not required for granular object level replication. Simply adding the database, while necessary initially, doesn't automatically grant the necessary privileges on contained objects.

NEW QUESTION # 341
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. Create a database replica in the EU region and manually refresh it periodically using 'CREATE DATABASE AS CLONE'
  • B. Use Snowflake's Data Sharing feature to share data from the US region with an account in the EU region. This automatically replicates the data.
  • C. Enable database replication using replication groups, configure a primary database in us-east-I , and a secondary database in eu-west-l. Set the replication schedule with 'ALTER REPLICATION GROUP ADD .
  • D. Manually unload data from the US region and load it into the EU region using SnowSQL. Automate this process using a scheduled task.
  • E. Export data from the US region to cloud storage (e.g., AWS S3 or Azure Blob Storage) and then load it into the EU region using Snowpipe.
Answer: C
Explanation:
Option B is the most suitable because it utilizes Snowflake's replication groups, which provide automated and continuous replication with minimal downtime during failover. Option A requires manual intervention. Option C doesn't truly replicate the data; it provides access to it. Options D and E are inefficient and introduce significant latency.

NEW QUESTION # 342
You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?
  • A. Increase the size of the Snowflake warehouse used for the Snowpark session.
  • B. Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
  • C. Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
  • D. Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
  • E. Disable auto-tuning for the warehouse to ensure consistent performance
Answer: C
Explanation:
Option D provides the most significant potential improvement. Clustering the target table enables Snowflake to efficiently organize and store the data based on the clustering key. Pre-sorting the data according to the clustering key before writing ensures that Snowflake can write the data in the optimal order, minimizing the need for reorganization and improving write performance. Increasing the warehouse size (A) can help, but clustering is a more targeted solution. Reducing file size (B) might slightly improve things, but it's not the primary bottleneck. Repartitioning (C) can help with parallelism, but only if the data is also sorted according to the clustering key. Disabling autotuning (E) could actually hurt performance.

NEW QUESTION # 343
You are tasked with loading a large CSV file (1 T B) into Snowflake. The file contains data for the past 5 years, partitioned by year in the filename (e.g., 'data 2019.csv', 'data 2020.csv', etc.). You need to minimize data loading time and ensure data quality. You have a Snowflake virtual warehouse 'XSMALL' and a stage 'my_stage'. Which of the following strategies would be MOST effective?
  • A. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = CONTINUE option. Implement data quality checks post-load using SQL queries.
  • B. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = ABORT STATEMENT option. Create a file format with "SKIP HEADER = 1' and 'TRIM SPACE = TRUE.
  • C. Use Snowpipe with auto-ingest enabled. Ensure your cloud storage event notifications are properly configured. Create a file format with 'SKIP HEADER = 1' and 'TRIM SPACE = TRUE Leave the warehouse as 'XSMALL' to control costs.
  • D. Create multiple named file formats each with a unique 'SKIP HEADER value matching the number of header rows in each file. Load using a single 'COPY' command referencing each file format specifically.
  • E. Load each file individually using a separate 'COPY' command with 'VALIDATION MODE = RETURN ERRORS to check for data quality issues before loading the next file. Use the 'XSMALL' warehouse for all loads.
Answer: A
Explanation:
Option B is the most effective. Increasing the warehouse size to 'LARGE allows for parallel processing and faster loading. ERROR = CONTINUE ensures that the load process doesn't halt on minor errors, and post-load data quality checks are more efficient. A allows validation during load which slows down the process significantly. C will halt the entire process upon encountering an error. D is not suitable because it will be throttled by the XSMALL warehouse, which is not good for initial data loading. E isn't realistic as files should have a standard headen

NEW QUESTION # 344
A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?
  • A. Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
  • B. Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.
  • C. Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.
  • D. Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.
  • E. Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
Answer: E
Explanation:
Conditional masking using the CURRENT ROLE() function within masking policies is the most efficient and secure approach. It allows a single table to be used while dynamically controlling data visibility based on the user's role. Views (Option A) can introduce maintenance overhead. Encryption (Option C) is generally used for data at rest and in transit and is not the correct solution for masking. Tag-based masking can add complexity when direct role-based masking is simpler. Replicating the table (Option E) would consume a huge amount of resource, and increase data duplication issues.

NEW QUESTION # 345
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