最新のDEA-C02一発合格を取得 - Jpshiken内のすべて

ちなみに、Jpshiken DEA-C02の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1aRtcfnHgY1gPcFbzvVeExi8YoGp0syI6

すべての顧客のニーズを満たすために、当社はこの分野で多くの主要な専門家と教授を採用しました。これらの専門家と教授は、お客様向けに高品質のDEA-C02試験問題を設計しました。当社の製品がすべての人々に適していると約束できます。 DEA-C02実践教材を購入して真剣に検討する限り、短時間で試験に合格して認定を取得することをお約束します。 DEA-C02試験の質問を選択してレビューに役立ててください。DEA-C02スタディガイドから多くのメリットを得ることができます。

被験者は定期的に計画を立て、自分の状況に応じて目標を設定し、研究を監視および評価することにより、学習者のプロフィールを充実させる必要があります。 DEA-C02試験の準備に役立つからです。試験に合格して関連する試験を受けるには、適切な学習プログラムを設定する必要があります。当社からDEA-C02テストガイドを購入し、それを真剣に検討すると、最短時間でDEA-C02試験に合格するのに役立つ適切な学習プランが得られると考えています。

>> DEA-C02一発合格 <<

DEA-C02日本語 & DEA-C02復習資料

若者はより大きな雇用圧力に直面しています。競争力を高めることが不可欠です。私たちのDEA-C02試験資料を選択することで、日々の仕事であなたの問題を解決できます。より実用的なスキルを得ることもできます。私たちのDEA-C02試験資料には最新の知識と情報が含まれています。さらに、最も権威があるDEA-C02認定試験資格証明書を取得することができます。だから、多くの人を引きつけます。

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q237-Q242):

質問 # 237
You are tasked with optimizing a Snowpipe Streaming pipeline that ingests data from Kafka into a Snowflake table named 'ORDERS' You notice that while the Kafka topic has high throughput, the data ingestion into Snowflake is lagging. The pipe definition is as follows: "sql CREATE OR REPLACE PIPE ORDERS_PIPEAS COPY INTO ORDERS FROM @KAFKA STAGE FILE_FORMAT = (TYPE = JSON); Which of the following actions, taken individually, would be MOST effective in improving the ingestion rate, assuming sufficient compute resources are available in your Snowflake virtual warehouse?

A. Increase the size of the virtual warehouse associated with the Snowflake account.
B. Tune the parameter in the file format definition to a smaller value.
C. Enable auto-ingest for the Snowpipe.
D. Implement batching within the Kafka producer to send larger messages.
E. Increase the number of Kafka partitions and ensure Snowflake has enough compute to consume them in parallel using Snowpipe Streaming.

正解：E

解説：
Snowpipe Streaming directly ingests data without staging files, making A, B, C, and D less relevant. Increasing Kafka partitions and ensuring parallel consumption by Snowflake leverages the distributed nature of Kafka and Snowpipe Streaming, providing the most significant performance improvement for high throughput scenarios.

質問 # 238
A data engineer is responsible for a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a data warehouse. The engineer needs to implement a notification system to alert them when specific data quality issues occur, such as data duplication exceeding a threshold or a sudden drop in data volume. Which approach offers the MOST flexible and scalable solution for implementing these notifications?

A. Rely solely on Snowflake's Data Sharing feature to share the data with a data quality team who will manually review the data and report any issues.
B. Implement a data quality monitoring tool that integrates with Snowflake via JDBC/ODBC and uses its own rules engine and notification system to detect and alert on data quality issues.
C. Develop custom SQL scripts to periodically query the data for quality issues and send email notifications using Snowflake's stored procedures and the 'EMAIL' external function.
D. Use Snowflake's built-in resource monitors to track data volume and configure alerts based on predefined thresholds. This approach is simple but limited in its ability to detect complex data quality issues.
E. Create a series of Snowflake Tasks that execute SQL queries to check for data quality issues. If an issue is detected, the task triggers an external function to send a notification to a messaging service (e.g., AWS SNS, Azure Event Grid).

正解：B、E

解説：
Options C and D provide the most flexible and scalable solutions. Using a dedicated data quality monitoring tool (C) offers a comprehensive solution with a rules engine and notification system. Option D allows for a modular and scalable approach where individual tasks check for specific data quality issues and trigger notifications via external functions. Option A is too limited. Option B lacks scalability and maintainability compared to dedicated tools or task-based solutions. Option E is a manual and inefficient approach.

質問 # 239
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

A. Option D
B. Option A
C. Option E
D. Option C
E. Option B

正解：B

解説：
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.

質問 # 240
You are using the Snowflake Developer API to automate the creation and management of masking policies. You need to create a masking policy that masks an email address using SHA256 hashing. You also want to ensure that the policy can be applied to multiple tables and columns without modification. Assuming you have already established a connection to Snowflake using the Developer API, which of the following code snippets BEST demonstrates how to create and apply this masking policy using Python?

A. Option D
B. Option E
C. Option A
D. Option C
E. Option B

正解：B

解説：
Option E is the best approach because: It uses 'CREATE OR REPLACE MASKING POLICY', which allows you to update the policy definition if it already exists. It defines the input parameter as 'ANY' and then casts it to 'VARCHAR within the SHA2 function. This makes the policy more versatile and applicable to columns with different data types (as long as they can be cast to VARCHAR). Using CREATE OR REPLACE MASKING POLICY ensure no error will be raised when the policy already exist. The 'val ANY part ensures that policy work for all type of data.

質問 # 241
You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A. The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
B. The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
C. The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
D. DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
E. The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.

正解：A、C

解説：
A Stream becomes stale if its offset is beyond the retention period. If the task isn't running often enough (B), the Stream can exceed the retention period before being consumed. DDL operations (C) invalidate Streams. Option A is incorrect because SHOW INITIAL ROWS only impacts the first read, not staleness. D is partially incorrect. While Streams do have a maximum age, increasing the retention period or running the task more frequently is preferred. E is wrong because decreasing retention won't help prevent the error and only lead to data losses.

質問 # 242
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オンライン版はあらゆる電子機器に公開されています。同時に、DEA-C02学習資料のオンライン版はオフライン状態でも使用できます。オンライン状態にあるときに初めてオンラインバージョンを使用する必要があります。 DEA-C02学習教材のバージョンをオフラインで使用する権利があります。また、DEA-C02の学習教材をさらに検討する場合は、短時間でDEA-C02試験に簡単に合格する必要があります。

DEA-C02日本語: https://www.jpshiken.com/DEA-C02_shiken.html

専門家グループは、DEA-C02試験実践ガイドの研究と革新を専門とし、最新の革新と研究結果をDEA-C02クイズ準備にタイムリーに補足します、Snowflake DEA-C02一発合格誰もが現代社会で忙しいです、従って、もしあなたはうまく試験にパスし認定を取得したいなら、遠慮なく我々のDEA-C02練習資料を購入します、Snowflake DEA-C02一発合格では、この試験に合格するためにどのように試験の準備をしているのですか、DEA-C02試験準備を取得した後、試験期間中に大きなストレスにさらされることはありません、Snowflake DEA-C02一発合格弊社は我々の商品に自信がありますから、更新と返金の承諾をしています、ただし、当社からDEA-C02テストプラクティスファイルを購入することに決めた場合は、ここ数年で行った最良の決定の1つになることをお知らせします。

人々は、存在が最も一般的なものであるため、存在の説明もそれ以上の議論なしにそれDEA-C02自体から来ていると考えています、俺にこの話をしたのは叔父の峡で、それは今年の春から夏にかけての藤野谷家と佐井家、それに俺の両親をめぐる騒動のあとのことだった。

実際的なDEA-C02一発合格一回合格-権威のあるDEA-C02日本語

専門家グループは、DEA-C02試験実践ガイドの研究と革新を専門とし、最新の革新と研究結果をDEA-C02クイズ準備にタイムリーに補足します、誰もが現代社会で忙しいです、従って、もしあなたはうまく試験にパスし認定を取得したいなら、遠慮なく我々のDEA-C02練習資料を購入します。

では、この試験に合格するためにどのように試験の準備をしているのですか、DEA-C02試験準備を取得した後、試験期間中に大きなストレスにさらされることはありません。

ちなみに、Jpshiken DEA-C02の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1aRtcfnHgY1gPcFbzvVeExi8YoGp0syI6