DEA-C01試験の準備方法｜正確的なDEA-C01資格認証攻略試験｜素敵なSnowPro Advanced: Data Engineer Certification Exam日本語版参考資料

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Exam Format:	Scenario-based questions, Multiple choice, Multiple select
Certificate Validity Period:	2 years
Real Exam Qty:	Approximately 65 questions
Exam Price:	$375 USD
Available Languages:	English
Related Certifications:	SnowPro Core Certification
Exam Duration:	115 minutes
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Recommended Training:	SnowPro Advanced Data Engineer Exam Guide Snowflake University Training
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 1	ストレージとデータ保護: このトピックでは、データ回復機能の実装と、Snowflake のタイムトラベルおよびマイクロパーティションの理解をテストします。エンジニアは、クローン作成を通じて新しい環境を作成し、データ保護を確保する能力について評価され、Snowflake データの整合性とアクセス性を維持するための必須スキルが強調されます。
トピック 2	パフォーマンスの最適化: このトピックでは、Snowflake でパフォーマンスの低いクエリを最適化およびトラブルシューティングする能力を評価します。候補者は、最適なソリューションの構成、キャッシュの利用、およびデータパイプラインの監視に関する知識を証明する必要があります。このトピックでは、エンジニアが特定のシナリオに基づいてパフォーマンスを向上できるようにすることに重点を置いています。これは、Snowflake データエンジニアとソフトウェアエンジニアにとって重要です。
トピック 3	データ移動: Snowflake データエンジニアとソフトウェアエンジニアは、Snowflake でのデータの読み込み、取り込み、トラブルシューティングの能力に基づいて評価されます。継続的なデータパイプラインの構築、コネクタの構成、データ共有ソリューションの設計に関するスキルを評価します。
トピック 4	データ変換: SnowPro Advanced: Data Engineer 試験では、ユーザー定義関数 (UDF)、外部関数、およびストアドプロシージャの使用スキルを評価します。半構造化データを処理し、変換に Snowpark を活用する能力を評価します。このセクションでは、Snowflake エンジニアがデータ操作タスクに不可欠な Snowflake 環境内でデータを効果的に変換できることを保証します。
トピック 5	セキュリティ: DEA-C01 テストのセキュリティトピックでは、システムロールの管理やデータガバナンスなど、Snowflake セキュリティの原則について学習します。Snowflake データエンジニアとソフトウェアエンジニアにとって安全なデータ環境を維持するために重要な、データを保護し、ポリシーに準拠する能力を測定します。

Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q129-Q134):

質問 # 129
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?

A. TRUE
B. FALSE

正解：A

解説：
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions. The reason is that the owner of the data being shared does not typically control the users or roles in the account with which the view is being shared.

質問 # 130
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data. Which solution will meet this requirement in the MOST operationally efficient way?

A. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.
B. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
C. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
D. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.

正解：A

解説：
AWS Glue DataBrew provides built-in data profiling specifically for exploring dataset structure, data types, distributions, missing values, and anomalies before preparation. For a 100 MB Parquet file in Amazon S3, creating a profile job is the most operationally efficient approach because it requires minimal setup and no custom infrastructure or query framework.

質問 # 131
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?

A. Design the data source so events are not ingested into Kinesis Data Streams multiple times.
B. Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.
C. Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.
D. Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

正解：B

質問 # 132
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?

A. Calculate the clustering depth for a table using mentioned columns in the table.
B. An error: this function does not accept predicates ('C9 = 30') as parameter.
C. An error: this function does not accept lists of columns as a third parameter.
D. Calculate the clustering depth for a table using the clustering key defined for the table.

正解：A

質問 # 133
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

A. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
B. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.
C. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
D. Set the FORCE option to load all files, ignoring load metadata if it exists.

正解：B

解説：
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files

質問 # 134
......

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