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| Section | Objectives |
|---|---|
| Performance and Optimization | - Query optimization techniques - Warehouse sizing and scaling - Clustering and partition strategies |
| Data Engineering Fundamentals | - Snowflake architecture for data engineering - Data pipelines concepts and patterns |
| Data Transformation and Processing | - Streams and Tasks for ELT pipelines - Handling semi-structured data (JSON, Avro, Parquet) - SQL-based transformations in Snowflake |
| Data Ingestion and Integration | - Batch and streaming ingestion approaches - Snowpipe usage and automation - Staging data and loading mechanisms |
| Security and Data Governance | - Secure data sharing - Role-based access control (RBAC) - Data masking and encryption |
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NEW QUESTION # 149
You have a Snowflake table 'ORDERS with columns 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, and 'TOTAL AMOUNT. You notice that many queries filtering by 'ORDER DATE are slow, even after enabling query acceleration. You decide to implement a caching strategy to improve performance. Which of the following approaches will be most effective in leveraging Snowflake's caching capabilities and improving the performance of date-filtered queries, especially when the data volume for each date is large and varied? Assume virtual warehouse is medium size.
Answer: D
Explanation:
Clustering on 'ORDER DATE' (A) is the most effective approach. Clustering physically organizes the data, allowing Snowflake to efficiently prune partitions and retrieve only the necessary data for date-filtered queries. Materialized views (B) are suitable for pre-aggregation, but less suitable for ad-hoc queries on different date ranges where only filtering is required. Increasing the data retention period (C) does not impact caching performance and may increase storage costs. 'RESULT_SCAN' (D) is session-specific and not persistent. Adding a WHERE clause (E) helps, but is not as impactful as Clustering.
NEW QUESTION # 150
A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:
This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?
Answer: C
Explanation:
Option A provides the best performance because it pre-computes the aggregation for all time, allowing Snowflake to rewrite the query. Option B adds a WHERE clause that limits the data, negating the benefits of materialized view rewrite. Option C using 'REFRESH COMPLETE ON DEMAND is not ideal for near real-time requirements. Option D filters based on a very short time period and not aligned with original problem where the window is 7 days. Option E calculates SUM and COUNT instead of AVG, doesn't match required output.
NEW QUESTION # 151
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.
Answer: B,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 # 152
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 ?
Answer: D
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 # 153
You have configured a replication group to replicate a database 'CUSTOMER DATA' from your primary Snowflake account (AWS us- east-I) to your secondary Snowflake account (Azure eastus). After a recent network outage, the replication process stopped. Upon investigation, you find that some tables in the 'CUSTOMER DATA' database in the primary account have been modified (schema changes). You need to resume replication and ensure data consistency in the secondary account, with minimal impact on users querying the secondary database. Which of the following commands or sequence of commands would be the MOST appropriate to refresh the secondary database while minimizing downtime for query users?
Answer: E
Explanation:
Option D provides the most efficient and least disruptive approach. The 'ALTER DATABASE ... REFRESH FROM' command allows you to refresh a secondary database from the primary, automatically handling schema changes and ensuring data consistency. Option A is not sufficient as simply refreshing the replication group might not fully synchronize schema changes, leading to potential inconsistencies. Option B is the most disruptive option as it drops and recreates the secondary database, causing significant downtime. The is in readonly when it's configured and can be refreshed using 'ALTER DATABASE REFRESH FROM.;'
NEW QUESTION # 154
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