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| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Architecture and Design | 15-20% | - Apply design patterns for data engineering workloads - Integrate with external tools and platforms: orchestration, BI, ML - Design scalable, reliable, and maintainable data pipelines - Build end-to-end near real-time streaming solutions |
| Data Transformation and Processing | 20-25% | - Process semi-structured data: JSON, Avro, Parquet, ORC - 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 |
| 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 |
| Performance Optimization and Compute Management | 15-20% | - Monitor and tune workloads and resource utilization - Optimize query performance: clustering, partitioning, materialized views - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control - Use search optimization and query acceleration services |
| Data Ingestion and Sourcing | 20-25% | - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Handle different data formats: structured, semi-structured, unstructured - Design and implement continuous and batch ingestion pipelines - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions |
| Data Governance, Security, and Compliance | 10-15% | - Implement access control: RBAC, authentication, authorization - Enforce data quality and governance standards - Apply data protection: encryption, masking, row-level security - Manage data lineage, cataloging, and compliance policies |
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NEW QUESTION # 159
You have a 'WEB EVENTS' table that stores user activity on a website. It includes columns like 'USER ID, 'EVENT TYPE , EVENT TIMESTAMP, and 'PAGE URL'. You need to create a materialized view that calculates the number of distinct users visiting each page daily. You are also tasked with minimizing the impact on the underlying 'WEB EVENTS' table during materialized view refreshes, as other critical processes rely on it. Which of the following strategies would provide the MOST efficient solution, considering both performance and concurrency?
Answer: E
Explanation:
Option D provides the most efficient solution. Incremental refreshes are designed to efficiently update the materialized view with only the changes from the base table, minimizing the impact on the 'WEB_EVENTS' table. Options A, B and E might lock the table for longer periods. Option C is not a valid option in Snowflake. Option E is not efficient since it involves truncating and reloading the materialized view, consuming unnecessary resources and being potentially slow.
NEW QUESTION # 160
You're tasked with optimizing a Snowflake data pipeline that transforms and loads data into a target table. The pipeline uses a series of complex SQL queries with multiple joins and aggregations. After analyzing the query execution plans, you identify a few key bottlenecks. Which of the following optimization techniques would MOST directly address common performance bottlenecks in such a data pipeline within Snowflake?
Answer: B,D
Explanation:
Options B and D are the most effective. Clustering keys optimize data retrieval based on filter conditions, significantly improving query performance, and materialized views reduce compute costs by storing pre-computed data and the optimizer rewrites to use it where possible, reducing processing time for queries that require those aggregations. Option A is a brute force approach that may not be cost effective. Option C is generally incorrect because it is not the most cost effecient method. Option E would negatively affect performance.
NEW QUESTION # 161
You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.
Answer: E
Explanation:
Option A correctly uses Snowpark to extract and transform the JSON data. It utilizes the correct syntax for extracting values from the column using the colon notation It then uses 'to_date', and 'to_float' to perform the necessary data type conversions. Finally, it saves the transformed DataFrame to a new table called 'CLEAN ORDERS' using overwrite mode. Option B uses incorrect syntax with 'col('ORDER_DETAlLS:order_id')'. Option C uses the 'get()' function, which is more appropriate when the JSON structure is unknown, and using colon notation is simpler and faster when the JSON structure is known. Option D attempts to use , which returns a JSON object that can be accessed with square brackets. The correct Snowflake function to access JSON properties in this manner without parsing, is through the colon notation. Option E uses which is a Snowflake SQL function, not a native Snowpark function, and requires JSON path, making it less efficient than direct extraction using Although E will work, A is more optimized way to write code.
NEW QUESTION # 162
You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?
Answer: B,D,E
Explanation:
Options B, C and D are the most useful. Option B leverages Time Travel to compare data at a specific point. Option C creates an alert to check the difference in total amount. Option D prevents inconsistent data from ever entering the table. Option A attempts to fix an issue, rather than preventing it and a small warehouse will take too long to complete. Option E restricts table access, but does not resolve inconsistent data, which is not what the prompt requests.
NEW QUESTION # 163
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?
Answer: E
Explanation:
Clustering (E) optimizes the physical storage of data, making it more efficient to retrieve specific historical versions. Decreasing data retention (C) can improve performance but reduces the available time travel window. Increasing retention (A) will degrade performance even more. Materialized Views (B) can help, but they require maintenance and may not be suitable for all time travel scenarios. Cloning (D) creates a full copy, which is inefficient and may not be necessary.
NEW QUESTION # 164
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