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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple Choice, Multiple Select |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Passing Score: | 750 / 1000 |
| Exam Price: | USD 375 |
| Related Certifications: | SnowPro Advanced: Data Engineer SnowPro Core SnowPro Advanced: Data Analyst |
| Available Languages: | English |
| Recommended Training: | Snowflake Advanced Architect Learning Path Snowflake Documentation |
| Exam Registration: | Pearson VUE Registration Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored or Onsite testing center |
| Pre Condition: | Recommended: SnowPro Core certification + 2+ years hands-on experience as Snowflake Architect in production environment |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/ |
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a certification exam that validates the skills and knowledge required to design and implement complex Snowflake solutions. It is a professional-level certification that is designed for architects who have extensive experience working with Snowflake and want to demonstrate their expertise in the field. ARA-C01 Exam Tests the aspirant's ability to design, architect, and implement Snowflake solutions that meet complex business requirements.
NEW QUESTION # 53
Which statement is not true about shared database?
Answer: A
NEW QUESTION # 54
Search optimization does not support Materialized views and External Tables
Answer: A
NEW QUESTION # 55
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
Answer: B
Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
* [CREATE TASK | Snowflake Documentation]
* [Using Streams and Tasks | Snowflake Documentation]
* [CREATE WAREHOUSE | Snowflake Documentation]
NEW QUESTION # 56
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)
Answer: C,E
Explanation:
These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
Reference:
Snowflake Data Lake Architecture | Snowflake Documentation
Snowflake Data Lake Best Practices | Snowflake Documentation
NEW QUESTION # 57
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)
Answer: A,C
Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
* Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
* Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
* Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
* Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
* Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
* Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
* Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
References:
* Clustering Keys & Clustered Tables | Snowflake Documentation
* [Considerations for Choosing Clustering for a Table | Snowflake Documentation]
NEW QUESTION # 58
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