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| Section | Weight | Objectives |
|---|---|---|
| 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 Ingestion and Sourcing | 20-25% | - Handle different data formats: structured, semi-structured, unstructured - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions - Design and implement continuous and batch ingestion pipelines |
| Data Sharing and Collaboration | 5-10% | - Design multi-tenant and cross-account data architectures - Implement secure data sharing and data exchanges - Work with Snowflake Data Marketplace and external data providers |
| Performance Optimization and Compute Management | 15-20% | - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control - Monitor and tune workloads and resource utilization - Use search optimization and query acceleration services - Optimize query performance: clustering, partitioning, materialized views |
| Data Pipeline Architecture and Design | 15-20% | - Integrate with external tools and platforms: orchestration, BI, ML - Design scalable, reliable, and maintainable data pipelines - Apply design patterns for data engineering workloads - Build end-to-end near real-time streaming solutions |
| Data Governance, Security, and Compliance | 10-15% | - Manage data lineage, cataloging, and compliance policies - Implement access control: RBAC, authentication, authorization - Enforce data quality and governance standards - Apply data protection: encryption, masking, row-level security |
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NEW QUESTION # 278
You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?
Answer: D
Explanation:
Option B is the most efficient. Using 'COPY INTO' to export to S3 is a fast and optimized way to move data. Truncating the table is faster than deleting a large number of rows. Creating an external table allows you to query the archived data in S3 if needed, without ingesting it into Snowflake. Options A & C create another Snowflake table which will consume snowflake credits and storage, which might be costly for a long term storage. Option D cloning is an expensive operation. Option E Partitioning in Snowflake is not natively supported, and would require manual management using external tables and views.
NEW QUESTION # 279
Consider the following scenario: You have a Snowflake task that refreshes a materialized view. The materialized view is based on a large base table that is constantly being updated. The refresh operation is taking longer than expected, impacting downstream reporting. Which of the following actions will likely NOT improve the performance of the materialized view refresh?
Answer: E
Explanation:
Adding more columns to the materialized view (Option C) will generally worsen the performance of refresh operations, as it increases the amount of data that needs to be processed and stored. The materialized view only needs the minimal set of columns required by its query. All other options will likely improve performance or provide data to improve performance. Option A allows for more resources to be used during the refresh. Option B reduces the amount of data that needs to be scanned. Option D automates the refresh process, making it more efficient overall and leverages Snowflake's automatic refresh optimization. Option E: by analyzing the query profile, data engineers can uncover specific areas for improvement within the query execution plan.
NEW QUESTION # 280
You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?
Answer: C
Explanation:
Option D is generally the most efficient. Converting Avro to JSON before ingestion simplifies the integration with Snowpipe and Snowpark. Snowpipe is optimized for semi-structured data like JSON within a VARIANT column. Subsequently, Snowpark DataFrames can easily process the JSON data using built-in functions, avoiding the complexity and potential performance bottlenecks of UDFs (Option B) or custom connectors (Option A). Although Snowflake's function can work with variant data, operating on raw Avro data is not natively supported by Snowpipe without pre-processing or complex UDF logic. External functions (Option E) add another layer of complexity for data retrieval.
NEW QUESTION # 281
You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)
Answer: A,B,D
Explanation:
Options B, C, and D are correct. Logging intermediate results allows you to track the flow of data and identify the point of failure. Modularizing the code and testing individual functions makes debugging easier. Passing the 'snowflake' binding allows each module to log and execute SQL. Option A is insufficient, complex error requires more granular logging. Option E is dangerous and should be avoided as metadata changes should ideally be performed outside of transformation stored procedure and should be managed by orchestration tools.
NEW QUESTION # 282
A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?
Answer: B,E
Explanation:
Creating a clustering key on 'PRODUCT ID and 'TRANSACTION DATE' allows Snowflake to efficiently prune micro-partitions based on the date range filter, and then quickly group by "PRODUCT_ID. A materialized view pre-aggregates the data, significantly reducing the amount of computation required at query time. While increasing the warehouse size might provide some improvement, it is not the most efficient solution. Converting 'TRANSACTION_DATE to VARCHAR is detrimental. Using a temporary table is not necessarily an optimization.
NEW QUESTION # 283
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