さらに、GoShiken ARA-C01ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1TYxtuhQNBIJ6xeOdpl5xP4eZSQ5eMCMa
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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Available Languages: | English |
| Passing Score: | 750 (scaled score out of 1000) |
| Related Certifications: | SnowPro Core Certification |
| Exam Price: | $375 USD |
| Exam Duration: | 115 minutes |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | Approximately 65 |
| Recommended Training: | Snowflake Training & Certification Learning Path |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | SnowPro Core Certification is strongly recommended and often expected before attempting the Advanced Architect exam |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
我々は全て平凡かつ普通な人で、時には勉強したものをこなしきれないですから、忘れがちになります。 GoShikenのSnowflakeのARA-C01試験トレーニング資料を見つけたら、これはあなたが購入しなければならないものを知ります。GoShikenはあなたが楽に試験に合格することを助けます。GoShikenを信頼してください。どんなに難しい試験でも、GoShikenがいるのなら、大丈夫になります。
Snowflake ARA-C01認定試験は、かなりの量の準備と研究を必要とする厳格な試験です。候補者は、スノーフレークの建築、ベストプラクティス、デザインの原則を深く理解することが期待されています。この試験では、候補者がスノーフレークと複雑な問題をトラブルシューティングする能力を実践的に経験する必要があります。この認定は、建築家が雪片の専門知識を実証し、仲間と差別化する優れた方法です。
質問 # 44
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)
正解：A、C
解説：
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
Reference:
Snowpipe Overview | Snowflake Documentation
Materialized Views on External Tables | Snowflake Documentation
質問 # 45
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?
正解：C
解説：
The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2 The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Using the GET Command
3: Snowflake Documentation | Using the Snowflake Connector for Python
4: Snowflake Documentation | Using the Snowflake API
: Snowflake Documentation | Using the GET Command in Snowsight
: SnowPro Advanced: Architect | Study Guide
: Using the GET Command
: Using the Snowflake Connector for Python
: Using the Snowflake API
: [Using the GET Command in Snowsight]
質問 # 46
An Architect is designing a solution that will be used to process changed records in an orders table. Newly-inserted orders must be loaded into the f_orders fact table, which will aggregate all the orders by multiple dimensions (time, region, channel, etc.). Existing orders can be updated by the sales department within 30 days after the order creation. In case of an order update, the solution must perform two actions:
1. Update the order in the f_0RDERS fact table.
2. Load the changed order data into the special table ORDER _REPAIRS.
This table is used by the Accounting department once a month. If the order has been changed, the Accounting team needs to know the latest details and perform the necessary actions based on the data in the order_repairs table.
What data processing logic design will be the MOST performant?
正解：D
解説：
The most performant design for processing changed records, considering the need to both update records in the f_orders fact table and load changes into the order_repairs table, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to the f_orders fact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into the order_repairs table, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.
Reference: Snowflake's documentation on streams and tasks for handling data changes efficiently.
質問 # 47
An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO <location> command.
Which configuration is valid?
正解：D
解説：
For the configuration of data unloading in Snowflake, the valid option among the provided choices is "C." This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO <location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms.
Reference:
Snowflake Documentation on COPY INTO command
Snowflake Documentation on Supported File Formats
Snowflake Documentation on Compression and Encoding Options
質問 # 48
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?
正解：B、C、D
解説：
* The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
* CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
* SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
* ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
* Therefore, options A, B, and C are correct.
COPY INTO <table>
質問 # 49
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ARA-C01受験対策解説集: https://www.goshiken.com/Snowflake/ARA-C01-mondaishu.html
BONUS！！！ GoShiken ARA-C01ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=1TYxtuhQNBIJ6xeOdpl5xP4eZSQ5eMCMa