ARA-C01最新対策問題、ARA-C01無料問題

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Snowflake ARA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Accounts and Security25%- Governance and Compliance
  • 1. Object ownership and privilege management
  • 2. Data governance strategies
  • 3. Encryption and security architecture
- Access Control and Authentication
  • 1. SSO and MFA
  • 2. Role-based access control
  • 3. Network policies
  • 4. Data masking and row access policies
Data Engineering25%- Data Transformation
  • 1. Semi-structured data processing
  • 2. Data modeling strategies
  • 3. ETL and ELT design
- Data Loading and Pipelines
  • 1. Snowpipe and tasks
  • 2. Bulk and continuous data loading
  • 3. Streams and CDC
  • 4. External stages and file formats
Snowflake Architecture30%- Storage and Compute Design
  • 1. Micro-partitions and clustering
  • 2. Database and schema design
  • 3. Warehouse sizing and scaling
- Platform Architecture Design
  • 1. Cross-region and cross-cloud architecture
  • 2. Multi-account strategies
  • 3. Data sharing architecture
  • 4. Organization and account structure
Performance Optimization20%- Cost and Resource Optimization
  • 1. Concurrency scaling
  • 2. Resource monitors
  • 3. Warehouse cost optimization
- Query Performance
  • 1. Materialized views
  • 2. Query profiling and tuning
  • 3. Search optimization service
  • 4. Caching strategies

>> ARA-C01最新対策問題 <<

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Snowflake SnowPro Advanced Architect Certification 認定 ARA-C01 試験問題 (Q136-Q141):

質問 # 136
What considerations need to be taken when using database cloning as a tool for data lifecycle management in a development environment? (Select TWO).

正解：B、D


質問 # 137
An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports.
The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters:
AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium
What is the MOST cost-effective way to increase the availability of the reports?

正解：D

解説：
The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto-scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously.
References:Snowflake's official documentation on managing warehouses and using auto-scaling features.


質問 # 138
A large table is accessed by multiple teams using point-lookup queries on different columns. Query performance is poor.
What can be done to ensure good performance for all teams?

正解：D

解説：
When multiple teams perform selective point-lookups on different columns of the same large table, traditional clustering is not effective because a table can have only one clustering key. Creating a composite clustering key across many columns often leads to poor clustering depth and limited pruning benefits.
The Search Optimization Service (SOS) is specifically designed for this scenario (Answer C). It enables fast point-lookups across multiple columns without requiring table reclustering. SOS maintains additional search access paths that accelerate highly selective predicates and is ideal when queries vary across many lookup columns.
Materialized views introduce additional storage, maintenance overhead, and operational complexity, and Snowflake does not automatically replace base tables with materialized views. This question tests an architect' s ability to choose the right optimization technique for multi-access-path workloads, a core SnowPro Architect competency.
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質問 # 139
A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?

正解：C

解説：
A clustering key is a subset of columns or expressions that are used to co-locate the data in the same micro- partitions, which are the units of storage in Snowflake. Clustering can improve the performance of queries that filter on the clustering key columns, as it reduces the amount of data that needs to be scanned. The best choice for a clustering key depends on the query patterns and the data distribution in the table. In this case, the columns DeviceId, IOT_timestamp, and CustomerId are frequently used in the filter predicate for the select statement, which means they are good candidates for the clustering key. The columns City and DeviceManufacturer are often retrieved, but not filtered on, so they are not as important for the clustering key.
The column UniqueId is used for counting, but it is not a good choice for the clustering key, as it is likely to have a high cardinality and a uniform distribution, which means it will not help to co-locate the data.
Therefore, the best option is to use DeviceId and CustomerId as the clustering key, as they can help to prune the micro-partitions and speed up the queries. References: Clustering Keys & Clustered Tables, Micro- partitions & Data Clustering, A Complete Guide to Snowflake Clustering


質問 # 140
What step will im the performance of queries executed against an external table?

正解：C

解説：
Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute). Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2 The other options are not effective steps for improving the performance of queries executed against an external table:
* Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing. The file names are only used for creating the external table and displaying the query results3
* Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252. Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4
* Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Internal stages are used for loading data into internal tables, not external tables5 References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Partitioning External Tables
* 3: Snowflake Documentation | Creating External Tables
* 4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files
* 5: Snowflake Documentation | Overview of Stages
* : SnowPro Advanced: Architect | Study Guide
* : Partitioning External Tables
* : Creating External Tables
* : Supported File Formats and Compression for Staged Data Files
* : Overview of Stages


質問 # 141
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