ARA-C01試験の準備方法｜完璧なARA-C01復習問題集試験｜ユニークなSnowPro Advanced Architect Certification資格復習テキスト
P.S.Xhs1991がGoogle Driveで共有している無料の2025 Snowflake ARA-C01ダンプ：https://drive.google.com/open?id=1-MRSB1t1PDPu7I3oIV8D3WFYLJhA4QAg
すべての人々のニーズに応じて、当社の専門家と教授は、すべての顧客向けに3種類のARA-C01認定トレーニング資料を設計しました。 3つのバージョンは、すべてのお客様が操作するために非常に柔軟です。実際の必要性に応じて、今後の試験の準備に最も適したバージョンを選択できます。当社のすべてのARA-C01トレーニング資料は、3つのバージョンにあります。 3つのバージョンのARA-C01の最新の質問を使用して、今後の試験の準備をすることは非常に柔軟です。
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。
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ARA-C01資格復習テキスト & ARA-C01日本語版対応参考書
ほとんどの時間インターネットにアクセスできない場合、どこかに行く必要がある場合はオフライン状態ですが、ARA-C01試験のために学習したい場合。心配しないでください、私たちの製品はあなたの問題を解決するのに役立ちます。最新のARA-C01試験トレントは、能力を強化し、試験に合格し、認定を取得するのに非常に役立つと確信しています。嫌がらせから抜け出すために、ARA-C01学習教材は高品質で高い合格率を備えています。だから、今すぐ行動しましょう！ ARA-C01クイズ準備を使用してください。
Snowflake ARA-C01（Snowpro Advanced Architect認定）試験は、Snowflakeで働く専門家向けの名誉ある認定プログラムです。さまざまなシナリオで複雑なスノーフレークソリューションを設計および実装する候補者の能力を測定します。認定プログラムは、スノーフレーク環境におけるデータウェアハウジング、データモデリング、ETL、セキュリティ、パフォーマンス最適化のベストプラクティスに関する候補者の知識を検証するように設計されています。試験に合格することは、Snowflakeの高度な概念と技術に関する専門知識を実証し、競争力のあるデータ分析業界で自分自身を区別する素晴らしい方法です。
Snowflake SnowPro Advanced Architect Certification 認定 ARA-C01 試験問題 (Q157-Q162):
質問 # 157
What are characteristics of the use of transactions in Snowflake? (Select TWO).
- A. A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.
- B. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.
- C. A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.
- D. Explicit transactions can contain DDL, DML, and query statements.
- E. The autocommit setting can be changed inside a stored procedure.
正解：C、D
解説：
A: Snowflake's transactions can indeed include DDL (Data Definition Language), DML (Data Manipulation Language), and query statements. When executed within a transaction block, they all contribute to the atomicity of the transaction-either all of them commit together or none at all.C. Snowflake supports explicit transaction control through the use of the BEGIN TRANSACTION (or simply BEGIN) and COMMIT statements. Alternatively, the BEGIN WORK and COMMIT WORK syntax is also supported, which is a standard SQL syntax for initiating and ending transactions, respectively.Note: The END TRANSACTION statement is not used in Snowflake to end a transaction; the correct statement is COMMIT or COMMIT WORK.
質問 # 158
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI).
The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)
- A. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
- B. Use the Internal Tokenization feature to obfuscate sensitive data.
- C. Use, at minimum, the Business Critical edition of Snowflake.
- D. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
- E. Use the External Tokenization feature to obfuscate sensitive data.
- F. Avoid sharing data with partner organizations.
正解：C、D、E
解説：
* A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
* One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
* Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
* A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are
* generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
* Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
* Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.
* Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice.
Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.
References: : Snowflake's Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing
質問 # 159
For which use cases, will you use cross-cloud and cross-region replication?
- A. Data portability and account migrations
- B. All of these
- C. Business continuity and disaster recovery
- D. Secure data sharing across regions/cloud
正解：B
質問 # 160
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?
- A. The command will return a warning stating that the file has unmatched columns.
- B. The command will return an error.
- C. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
- D. The parameter will be ignored.
正解：D
解説：
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Reference:
The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
CASE_INSENSITIVE: The column names in the source data must match the column names in the target table, but the case is ignored.
NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
1: COPY INTO <table> | Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation
質問 # 161
select metadata$filename, metadata$file_row_number from @filestage/data1.json.gz;
Please select the correct statements for the above-mentioned query.
- A. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
- B. FILESTAGE is the file name, METADATA$FILE_ROW_NUMBER will give the path to the data file in the stage
- C. FILESTAGE is the stage name, METADATA$FILE_ROW_NUMBER will give the row number for each record in the container staged data file
正解：C
質問 # 162
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ARA-C01資格復習テキスト: https://www.xhs1991.com/ARA-C01.html
さらに、Xhs1991 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=1-MRSB1t1PDPu7I3oIV8D3WFYLJhA4QAg
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