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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Sharing and Collaboration	5-10%	- Implement secure data sharing and data exchanges - Work with Snowflake Data Marketplace and external data providers - Design multi-tenant and cross-account data architectures
Topic 2: Data Governance, Security, and Compliance	10-15%	- Manage data lineage, cataloging, and compliance policies - Apply data protection: encryption, masking, row-level security - Implement access control: RBAC, authentication, authorization - Enforce data quality and governance standards
Topic 3: Data Transformation and Processing	20-25%	- Manage data quality, validation, and deduplication - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables - Transform and enrich data using SQL, JavaScript, Python, and Snowpark - Process semi-structured data: JSON, Avro, Parquet, ORC
Topic 4: Data Ingestion and Sourcing	20-25%	- Design and implement continuous and batch ingestion pipelines - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Handle different data formats: structured, semi-structured, unstructured - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions
Topic 5: Performance Optimization and Compute Management	15-20%	- Monitor and tune workloads and resource utilization - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control - Use search optimization and query acceleration services - Optimize query performance: clustering, partitioning, materialized views
Topic 6: Data Pipeline Architecture and Design	15-20%	- Integrate with external tools and platforms: orchestration, BI, ML - Build end-to-end near real-time streaming solutions - Apply design patterns for data engineering workloads - Design scalable, reliable, and maintainable data pipelines

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A. Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
B. Restore the entire Snowflake account to a point in time before the incorrect transformation.
C. Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
D. Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
E. Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.

Answer: A,D

Explanation:
Options B and E are the correct methods for rolling back changes using Time Travel. Option B clones the table to the desired state and replaces the original, effectively rolling it back. Option E directly overwrites the original table with the historical data obtained through Time Travel. Option A is an extreme measure. Option C is for recovering dropped tables, not rolling back changes within a table. Option D does not directly rollback the table to the previous state, the correct data has to be loaded, that will take more time.

NEW QUESTION # 29
You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

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

Answer: B

Explanation:
Option B is the correct approach. It creates a row access policy that checks if a row exists in the where the username matches the current user and the region matches the 'REGION' column in the 'SALES_DATR table. 'ADD ROW ACCESS POLICY is the correct command to apply the policy. Options A is incorrect as it uses IN clause, which can become inefficient with large datasets. Option C uses 'SET' which is not a valid operation, and Option D uses 'MODIFY which is used for masking policy and not row access policy. Option E uses 'CURRENT ROLE instead of 'CURRENT USER' which is not the appropriate filter criteria.

NEW QUESTION # 30
You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A. Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
B. Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE
C. Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
D. Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
E. Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.

Answer: E

Explanation:
Option B is the most comprehensive solution for secure data loading and PII protection. 'TRANSFORM' and JavaScript UDF masking during load prevent PII from being stored unmasked, enhancing security. Implementing masking policies provides granular control over access to the sensitive data post-load. It's more secure to avoid storing sensitive information even temporarily. Option A encrypts the data in transit but doesn't address masking. Option C requires dynamic data masking, but masking during copy is optimal. The correct way to copy and mask PII data is using a JavaScript UDF to mask the PII data during the load process, and then implement Masking Policies on the table.

NEW QUESTION # 31
You're tasked with building a data pipeline using Snowpark Python to incrementally load data into a target table 'SALES SUMMARY from a source table 'RAW SALES. The pipeline needs to ensure that only new or updated records from 'RAW SALES are merged into 'SALES SUMMARY' based on a 'TRANSACTION ID'. You want to use Snowpark's 'MERGE' operation for this, but you also need to handle potential conflicts and log any rejected records to an error table 'SALES SUMMARY ERRORS'. Which of the following approaches offers the MOST robust and efficient solution for handling errors and ensuring data integrity within the MERGE statement?

A. Incorporate an 'ELSE clause in the 'MERGE' statement to capture records that do not satisfy the update or insert conditions due to data quality issues. Use this 'ELSE clause to insert rejected records into 'SALES SUMMARY ERRORS'
B. Use a single 'MERGE statement with 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses. Capture rejected records by leveraging the ' SYSTEM$PIPE STATUS function after the 'MERGE operation to identify rows that failed during the merge.
C. Employ the 'MERGE statement with 'WHEN MATCHED THEN UPDATE' and 'WHEN NOT MATCHED THEN INSERT clauses, and use a stored procedure that executes the 'MERGE statement and then conditionally inserts rejected records into the 'SALES SUMMARY ERRORS' table based on criteria defined within the stored procedure. This will use the table function on the output.
D. Utilize the 'WHEN MATCHED THEN UPDATE and 'WHEN NOT MATCHED THEN INSERT clauses with a 'WHERE' condition in each clause to filter out potentially problematic records. Log these filtered records to using a separate 'INSERT statement after the 'MERGE operation.
E. Use the 'WHEN MATCHED THEN UPDATE' clause to update existing records and the 'WHEN NOT MATCHED THEN INSERT clause to insert new records. Implement a separate process to periodically compare 'SALES_SUMMARY with 'RAW_SALES' to identify and log any inconsistencies.

Answer: C

Explanation:
Option E provides the most robust solution. Using a stored procedure to execute the MERGE allows for more complex error handling logic. Critically, the result_scan function of the MERGE query can then be used to identify and analyze the success or failure of each individual record processed within the MERGE. This avoids separate processes or post-merge comparisons and is therefore more robust. Option A requires a separate process for inconsistency checking, which is less efficient and may miss real-time errors. Options B, C, and D do not offer a reliable and atomic way to capture and log all rejected records. The SYSTEM$PIPE_STATUS function is relevant for Snowpipe, not direct MERGE operations.

NEW QUESTION # 32
A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A. Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.
B. Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
C. Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.
D. Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
E. Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.

Answer: A

Explanation:
Option E is the most secure and scalable approach. It tokenizes the data during the load process, minimizing the amount of time the raw data resides in Snowflake. Using a UDF in a masking policy (options A and C) tokenizes the data on read, meaning the raw data is stored in Snowflake. Option B, using a stored procedure and , can be less efficient for large datasets. Data sharing raw data (Option D) defeats the purpose of tokenization for the source environment.

NEW QUESTION # 33
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