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Associate-Data-Practitioner資格認定、Associate-Data-Practitionerソフトウエア

我々JpexamはGoogleのAssociate-Data-Practitioner試験問題集をリリースする以降、多くのお客様の好評を博したのは弊社にとって、大変な名誉なことです。また、我々はさらに認可を受けられるために、皆様の一切の要求を満足できて喜ぶ気持ちでずっと協力し、完備かつ精確のAssociate-Data-Practitioner試験問題集を開発するのに準備します。

Google Associate-Data-Practitioner 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• データ分析とプレゼンテーション: このドメインでは、BigQuery と Jupyter ノートブックを使用してデータの傾向、パターン、および洞察を特定するデータ アナリストの能力を評価します。候補者は、SQL クエリを定義および実行してレポートを生成し、ビジネス上の質問に対するデータを分析します。| データ パイプライン オーケストレーション: このセクションはデータ アナリストを対象としており、シンプルなデータ パイプラインの設計と実装に重点を置いています。候補者は、ビジネス ニーズに基づいて適切なデータ変換ツールを選択し、ELT と ETL のユース ケースを評価します。

トピック 2

• データの準備と取り込み: この試験セクションでは、Google Cloud Engineers のスキルを測定し、データの準備と処理について扱います。受験者は、ETL、ELT、ETLT などのさまざまなデータ操作方法の違いを理解します。また、適切なデータ転送ツールを選択し、データ品質を評価し、Cloud Data Fusion や BigQuery などのツールを使用してデータ クリーニングを実施します。測定される重要なスキルは、取り込み前にデータ品質を効果的に評価することです。

トピック 3

• データ管理: このドメインでは、アクセス制御とガバナンスを構成する Google データベース管理者のスキルを測定します。候補者は、Identity and Access Management (IAM) を使用して最小権限アクセスの原則を確立し、Cloud Storage のアクセス制御の方法を比較します。また、ライフサイクル管理ルールを構成して、データ保持を効果的に管理します。測定される重要なスキルは、Google Cloud サービス内の機密データへの適切なアクセス制御を確保することです。

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Associate-Data-Practitionerソフトウエア & Associate-Data-Practitioner試験準備

当社Googleの専門家は、Associate-Data-Practitionerトレーニング資料を毎日更新し、最新の更新をタイムリーに提供します。当社の製品および購入手順に関する疑問または質問がある場合は、いつでも当社のオンライン顧客サービス担当者にご連絡ください。古いクライアントに割引を提供します。購入前にAssociate-Data-Practitionerテスト問題を無料でダウンロードして試用できます。したがって、当社の製品には多くのメリットがあります。 Associate-Data-Practitioner試験問題を購入する前に、無料デモでAssociate-Data-Practitioner模擬テストの特性と機能を知ることができます。

Google Cloud Associate Data Practitioner 認定 Associate-Data-Practitioner 試験問題 (Q100-Q105):

質問 # 100
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

• A. Forward unacknowledged messages to a dead-letter topic.
• B. Retry messages until they are acknowledged.
  B Implement flow control on the subscribers
• C. Seek back to the last acknowledged message.

正解：A

解説：
Implementingflow control on the subscribersallows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

質問 # 101
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

• A. Use Dataform to define the transformations in SQLX.
• B. Use Dataflow with a custom template for the transformation logic.
• C. Use Dataproc to run the transformations on a cluster.
• D. Use Cloud Data Fusion to visually design and manage the pipeline.

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let's break it down:
* Option A: Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage).
Custom templates scale well but increase development cost and effort.
* Option B: Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn't reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
* Option C: Dataform uses SQLX for BigQuery ELT, not Spark. It's unsuitable for pre-load transformations of raw logs and doesn't leverage Spark skills.

質問 # 102
Your organization's business analysts require near real-time access to streaming dat a. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

• A. Disable BiqQuery query result caching.
• B. Modify the schema to use parameterized data types.
• C. Create materialized views.
• D. Create a scheduled query to calculate and store intermediate results.

正解：C

解説：
Creating materialized views is the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.

質問 # 103
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

• A. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
• B. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
• C. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
• D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

正解：B

解説：
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

質問 # 104
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

• A. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
• B. Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
• C. Use Cloud Scheduler to schedule the jobs to run.
• D. Use Cloud Tasks to schedule and run the jobs asynchronously.

正解：A

解説：
UsingCloud Composerto create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.
Extract from Google Documentation: From "Cloud Composer Overview" (https://cloud.google.com
/composer/docs):"Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery."

質問 # 105
......

GoogleのAssociate-Data-Practitioner認証試験の合格証は多くのIT者になる夢を持つ方がとりたいです。でも、その試験はITの専門知識と経験が必要なので、合格するために一般的にも大量の時間とエネルギーをかからなければならなくて、助簡単ではありません。Jpexamは素早く君のGoogle試験に関する知識を補充できて、君の時間とエネルギーが節約させるウェブサイトでございます。Jpexamのことに興味があったらネットで提供した部分資料をダウンロードしてください。

Associate-Data-Practitionerソフトウエア: https://www.jpexam.com/Associate-Data-Practitioner_exam.html

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