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Snowflake DEA-C02 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q215-Q220):

NEW QUESTION # 215
A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

Answer: B,C

Explanation:
Options A and D are correct. They both correctly define a window specification partitioned by 'product_id' and ordered by 'sale_date' . Both options then calculate the cumulative sum of 'sale_amount over this window. Option A defines the window and applies it to the dataframe whereas Option D applies it to the dataframe in one step. Options B and E are incorrect because the order of partitionBy and orderBy is incorrect when setting the window specification. Option C is incorrect as it does not apply a window function after the orderBy.

NEW QUESTION # 216
Consider a scenario where you have a Snowflake table named 'CUSTOMER DATA' containing customer IDs (INTEGER) and encrypted credit card numbers (VARCHAR). You need to create a secure JavaScript UDF to decrypt these credit card numbers using a custom encryption key stored securely within Snowflake's internal stage, and then mask all but the last four digits of the decrypted number for data protection. Which of the following actions are necessary to ensure both functionality and security while adhering to Snowflake's best practices for UDF development and security?

A. Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.
B. Store the encryption key in a separate file on an internal stage accessible only by the UDF's service account and load the key from the file within the UDF at runtime.
C. Store the encryption key directly within the JavaScript UDF code as a string variable.
D. Pass the encryption key as an argument to the UDF each time it is called.
E. Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.

Answer: A,B

Explanation:
Options B and D are the correct answers. Option B - Storing the encryption key in a file on an internal stage, accessible only by the UDF's service account, is a secure way to manage the key. Option D - Snowflake's Secure Vault (Secret) feature is designed specifically for securely storing and managing sensitive information like encryption keys. This is the most recommended approach. Options A and C are insecure and should be avoided. Option E defeats the purpose of encryption.

NEW QUESTION # 217
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?

A. Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
B. Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
C. Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.
D. Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
E. Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.

Answer: B,D

Explanation:
Option B correctly leverages an external function to encapsulate the logic of fetching and processing the JSON data from the URL. The external function promotes code reusability and reduces complexity in the view. Option E is also correct as it demonstrates how to process fetched JSON data and use a UDF to enhance the transformation. Option A involves a procedural approach that is less efficient than using an external function or pipes. Option C does not directly work with data URLs and is geared more towards data residing within Snowflake storage. Option D is incorrect because it creates the external function just for identifying errors and creates a stored procedure just to process the data.

NEW QUESTION # 218
A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A. Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
B. Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
C. Contact Snowflake support and request them to restore the table from a system-level backup.
D. Restore the entire Snowflake account to a previous point in time before the table was truncated.
E. Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.

Answer: A

Explanation:
Option D is the most efficient and reliable. Cloning the table using Time Travel to a point before the truncation allows quick recovery with minimal data loss. The clone can then replace the truncated table. Option A relies on Snowflake support, which can be slow. Option B, UNDROP TABLE command, if the data retention period has passed or data has been purged then we cannot use it. Option C is manual and error-prone. Option E is an extreme measure and impacts the entire account.

NEW QUESTION # 219
You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

A. Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
B. Create a secure view that joins the tables and share only the secure view using a data share.
C. Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
D. Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
E. Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.

Answer: B

Explanation:
Secure views are specifically designed for data sharing while protecting the underlying data sources. Sharing the secure view ensures that the consumer only sees the aggregated data and cannot access the underlying tables directly. Options A and D expose the underlying tables, increasing the risk of data leakage. Option C introduces latency due to the materialized view refresh. Option E adds unnecessary complexity and potential performance overhead.

NEW QUESTION # 220
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