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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a comprehensive certification program designed to test the advanced expertise of Snowflake architects. Snowflake is a cloud-based data warehousing and analytics platform that provides businesses with a scalable, secure, and cost-effective solution for big data management and analysis. The SnowPro Advanced Architect Certification Exam is aimed at professionals who have a deep understanding of the Snowflake platform and are capable of designing and implementing complex data warehousing and analytics solutions.
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Snowflake ARA-C01 or SnowPro Advanced Architect Certification Exam is a challenging and comprehensive test designed to evaluate the skills and knowledge of experienced architects in the use and implementation of Snowflake's cloud-based data warehousing solutions. ARA-C01 exam covers a wide range of topics, including data modeling, designing and optimizing data warehouses, security, performance tuning, and advanced analytics. Passing ARA-C01 Exam is a significant achievement that demonstrates a high level of expertise in Snowflake architecture and design.
NEW QUESTION # 101
A query shows a high number of bytes spilled to remote storage. The query includes a WHERE clause that filters out a large percentage of rows.
What actions can reduce spillage and improve performance? (Select TWO).
Answer: C,E
Explanation:
Bytes spilled to remote storage indicate memory pressure during query execution. Increasing the warehouse size provides more memory and compute resources, reducing the likelihood of spilling (Answer A).
Additionally, defining clustering keys on highly selective filter columns improves micro-partition pruning, which reduces the volume of data scanned and processed in memory (Answer C). Together, these actions address both the root cause (too much data scanned) and the symptom (insufficient memory).
Increasing clusters improves concurrency, not per-query memory. Primary and foreign keys are informational only in Snowflake and do not affect execution plans. Retention settings have no impact on query execution.
This question reinforces SnowPro Architect performance diagnostics: understand both physical execution (memory, spill) and logical data access (pruning).
NEW QUESTION # 102
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
Answer: A
Explanation:
The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:
Recognizing Disk Spilling
Managing the Kafka Connector
NEW QUESTION # 103
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?
Answer: A
Explanation:
* The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
* Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
* Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts
* running. The query queue time depends on the warehouse load, concurrency, and priority settings.
* Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
* Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
* Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
* What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.
NEW QUESTION # 104
You have a very large table which is already clustered on columns that are used to retrieve data from the table by a business group. The base table data does not change much. Another business group came to you and requested for a relatively small subset of data from the table which they will query using complex aggregation logic. You know that querying with those columns will take a lot of time because the table is not clustered on those columns.
What is the most optimal solution that you will suggest to the business team?
Answer: C
NEW QUESTION # 105
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)
Answer: A,D
Explanation:
According to the Snowflake documentation, to create a database from an inbound share, the user's role must have the following privileges:
The CREATE DATABASE privilege on the current account. This privilege allows the user to create a new database in the account1.
The IMPORT DATABASE privilege on the share. This privilege allows the user to import a database from the share into the account2. The other privileges listed are not relevant for this requirement. The IMPORT SHARE privilege is used to import a share into the account, not a database3. The IMPORT PRIVILEGES privilege is used to import the privileges granted on the shared objects, not the objects themselves2. The CREATE SHARE privilege is used to create a share to provide data to other accounts, not to consume data from other accounts4.
Reference:
CREATE DATABASE | Snowflake Documentation
Importing Data from a Share | Snowflake Documentation
Importing a Share | Snowflake Documentation
CREATE SHARE | Snowflake Documentation
NEW QUESTION # 106
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