Snowflake DEA-C01テキスト & DEA-C01認定内容

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Available Languages:	English
Exam Duration:	115 minutes
Real Exam Qty:	Approximately 65 questions
Exam Price:	$375 USD
Exam Format:	Multiple select, Scenario-based questions, Multiple choice
Related Certifications:	SnowPro Core Certification
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/

>> Snowflake DEA-C01テキスト <<

DEA-C01認定内容、DEA-C01受験料過去問

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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	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。
トピック 2	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 3	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 4	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 5	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。

Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q220-Q225):

質問 # 220
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:

The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

A. Create a local secondary index (LSI) on DriverID.
B. Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.
C. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
D. Create a filter expression that uses RiderID and RideStatus.

正解：B

解説：
To let drivers efficiently query only their completed rides, you need a global secondary index (GSI) with DriverID as the partition key (so queries can be targeted per driver) and RideStatus as the sort key (so you can query for "Completed" rides without scanning the full table). This avoids costly scans and supports fast, targeted lookups at scale.

質問 # 221
An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?

A. Publish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.
B. Publish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.
C. Publish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.
D. Publish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.

正解：B

解説：
Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.

質問 # 222
A company needs to build a data pipeline to process a 1-TB file from an Amazon S3 bucket. The pipeline needs to create three DataFrames based on business logic. The pipeline must save all three DataFrames to a second S3 bucket in parallel. The company needs to set the pipeline to be the target of an Amazon EventBridge rule that matches file uploads to the source S3 bucket.
Which solution will meet these requirements with the LEAST maintenance overhead?

A. Configure an AWS Step Functions state machine to initiate an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
B. Configure three AWS Lambda functions to process the business logic and to save the DataFrames to the destination S3 bucket in parallel.
C. Configure an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
D. Configure an Apache Spark Streaming application on Amazon EMR to process data from the S3 source bucket in batches, create DataFrames, and save the output to the destination S3 bucket.

正解：C

解説：
An AWS Glue workflow can be triggered by an EventBridge rule when a file is uploaded to the source S3 bucket, and it can orchestrate three AWS Glue jobs to run in parallel. This provides a fully managed, serverless approach for Spark-based DataFrame processing at the 1-TB scale while keeping orchestration and operational management minimal.

質問 # 223
A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A. Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
B. Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster.
Write the logs to a table in the Aurora MySQL database.
C. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
D. Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.

正解：C

解説：
Amazon Redshift provides an option to log and store all user activities and connection activities for compliance and auditing purposes. Configuring logging to an Amazon S3 bucket is the recommended approach because:
Amazon S3 is cost-effective, scalable, and durable for storing large volumes of logs over time.
Amazon Redshift natively supports logging to S3, allowing user and connection logs to be easily enabled and stored directly in the specified S3 bucket.
Storing logs in S3 meets compliance needs and provides an efficient way to manage, archive, and retrieve logs for security reviews or audits.

質問 # 224
Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE). What action he can take to recover the dropped Share?

A. Consumer accounts that have created databases from the share will still be able to que-ry these databases as Share is separate securable object & it's still accessible using time travel feature.
B. He can recreate a share with the same name as a previous share which does restore the databases created (by any consumers) from the share SF_SHARE.
C. By Executing UNDROP command he could possibly recover the dropped Share SF_SHARE & its associated Databases for immediate consumer access.
D. A dropped share cannot be restored. The share must be created again using the CRE-ATE SHARE command and then configured using GRANT <privilege> ... TO SHARE and ALTER SHARE.

正解：D

解説：
Explanation
You can drop a share at any time using the DROP SHARE command.
Dropping a share instantly invalidates all databases created from the share by consumer accounts.
All queries and other operations performed on these databases will no longer work.
After dropping a share, you can recreate it with the same name;
however, this does not restore any of the databases created from the share by consumer accounts.
The recreated share is treated as a new share and all consumer accounts must create a new database from the new share.

質問 # 225
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DEA-C01認定内容: https://www.mogiexam.com/DEA-C01-exam.html

さらに、MogiExam DEA-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=1j_wYBJZsMUhHlUKG9st-ehTZr3QN5HPh