SNOWFLAKE - DEA-C02 - SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02)–UPDATED NEW PRACTICE MATERIALS

Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02)–Updated New Practice Materials

Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02)–Updated New Practice Materials

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q189-Q194):

NEW QUESTION # 189
You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

  • A. Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
  • B. All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
  • C. Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
  • D. Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
  • E. External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.

Answer: A,B,E

Explanation:
Cloning a schema clones all objects within it (tables, views, UDFs, etc.). Cloning uses metadata cloning initially, so it doesn't immediately duplicate the data. Sequences resume from where they left off (if not used cloned schema). Tasks and streams are not cloned when cloning a schema, unless the clone is done at the database level. External tables are cloned, but the data files remain in their original cloud storage location.


NEW QUESTION # 190
You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

  • A. The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
  • B. The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.
  • C. The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
  • D. The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
  • E. The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.

Answer: A,B,C,E

Explanation:
All options except D are likely causes of performance degradation. A poorly indexed 'user_groups' table (A) will slow down policy evaluation. Complex policy logic (B) can also impact performance. Interference with data pruning (C) is a common issue with row access policies. Full table scans (E) can be exacerbated by the policy if data is not clustered appropriately. Users needing explicit privileges to 'user_groups' is not needed since the policy handles that; also using a secure view handles that as well.


NEW QUESTION # 191
You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

  • A. Ingest all log data into a single VARIANT column in a Snowflake table.
  • B. Create separate tables for each log data format (text, JSON, XML).
  • C. Pre-process the log data to convert all formats into a standardized JSON format before ingestion.
  • D. Use Snowflake's external functions to parse the log data during query execution.
  • E. Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required

Answer: A,D

Explanation:
Options A and D are the most effective. Storing all log data in a VARIANT column allows for flexibility in handling different formats. Using external functions during query execution allows for on-demand parsing and transformation, avoiding the need to pre-process or create multiple tables. Option B could be viable, but introduces overhead. Option C requires creating a lot of tables. Option E is more complex than it should be for generic use cases. Parsing during query execution with Snowflake's native features/External Functions in conjunction with variant is generally recommended.


NEW QUESTION # 192
Consider the following scenario: You are managing a Snowflake environment where users are running various queries with varying resource demands. You observe frequent warehouse resizing operations, leading to performance fluctuations and increased costs. Which of the following strategies, when implemented together, would BEST stabilize warehouse performance and minimize unnecessary resizing?

  • A. Monitor query history using Snowflake's web interface and identify query patterns that consistently require larger resources; recommend users refactor those queries. Set the warehouse auto-suspend to a very short duration (e.g., 1 minute) to ensure resources are released quickly when idle.
  • B. Disable auto-suspend for the warehouse to prevent it from shutting down and causing performance delays. Force users to manually resize the warehouse as needed using SALTER WAREHOUSE commands.
  • C. Enable Query Acceleration Service (QAS) for the warehouse. Set the warehouse size to Medium, regardless of the actual workload demands, and rely solely on QAS to handle performance variations.
  • D. Implement Resource Monitors to limit the daily credit consumption of the warehouse. Increase the warehouse size to accommodate all possible query demands and set the auto-suspend to a longer duration (e.g., 60 minutes).
  • E. Implement Query Tagging to categorize queries based on resource consumption. Analyze resource utilization patterns for different query categories. Adjust warehouse size and multi-cluster configuration based on these patterns, ensuring that a reasonable number of concurrent queries for each workload type is met.

Answer: E

Explanation:
Option D offers a comprehensive approach: Query Tagging allows for analyzing resource consumption patterns of different query types. This data drives informed decisions regarding warehouse size and multi-cluster configuration, aligning resources with actual needs. Resource Monitors control cost, auto-suspend setting makes sure performace fluctations are avoided and cost are minimised. Option A is partly correct regarding query refactoring but short auto-suspend can increase cost. Option B does not address the root cause of performance fluctuations (varying query demands). Option C relies solely on QAS, which might not be sufficient for stabilizing performance and addressing all types of resource bottlenecks. Option E is highly impractical and inefficient.


NEW QUESTION # 193
You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

  • A. Increase the warehouse size to improve query performance.
  • B. Add a search optimization strategy to the table on the 'event_time' column.
  • C. The table is already partitioned by 'event_time' , so there is no need for further optimization.
  • D. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
  • E. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.

Answer: B,D,E

Explanation:
Partitioning in Snowflake is primarily for data management and micro-partition elimination on exact matches, not range queries. Clustering (B) reorders the data for better performance with range-based queries. A materialized view (C) pre-computes the aggregation, significantly speeding up the specific query. A search optimization strategy (E) can improve performance without requiring a full table scan. Increasing warehouse size (D) may help to some extent but is not the most targeted optimization. Option A is incorrect because partitioning alone doesn't solve the range query performance issue.


NEW QUESTION # 194
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