Google Associate-Data-Practitioner資格勉強、Associate-Data-Practitionerテキスト

専門的な知識を十分に身に付けることは、あなたの人生に大いに役立ちます。知識の時代の到来により、さまざまな労働条件や学習条件で自分自身を証明するために、Googleなどの専門的な証明書が必要になります。したがって、有用な実践教材を選択する正しい判断を下すことは非常に重要です。ここでは、心から誠実にAssociate-Data-Practitioner実践教材をご紹介します。 Associate-Data-Practitionerスタディガイドを選択した試験受験者の合格率は98％を超えているため、Associate-Data-Practitionerの実際のテストは簡単なものになると確信しています。

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。
トピック 2	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 3	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。

>> Google Associate-Data-Practitioner資格勉強 <<

完璧-高品質なAssociate-Data-Practitioner資格勉強試験-試験の準備方法Associate-Data-Practitionerテキスト

もしあなたはまだGoogleのAssociate-Data-Practitioner試験に合格するのために悩まればFast2testは今あなたを助けることができます。Fast2testは高品質の学習資料をあなたを助けて優秀なGoogleのAssociate-Data-Practitioner会員の認証を得て、もしあなたはGoogle Associate-Data-Practitionerの認証試験を通して自分を高めるの選択を下ろして、Fast2testはとてもよい選択だと思います。

Google Cloud Associate Data Practitioner 認定 Associate-Data-Practitioner 試験問題 (Q66-Q71):

質問 # 66
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
B. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
C. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

正解：A

解説：
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.

質問 # 67
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
B. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

正解：B

解説：
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.

質問 # 68
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
B. Apply a filter to only show products with a positive profit margin.
C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

正解：A

解説：
Defining a new measure in LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:
measure: profit_margin {
type: number
sql: (revenue - cost) / revenue ;;
value_format: "0.0%"
}
This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.

質問 # 69
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

A. Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub
/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
B. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
C. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
D. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

正解：A

解説：
UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub
/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

質問 # 70
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
C. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
D. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

正解：B

解説：
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

質問 # 71
......

毎年のAssociate-Data-Practitioner試験問題は、テストの目的に基づいてまとめられています。すべての回答はテンプレートであり、2つのパートの主観的および客観的なAssociate-Data-Practitioner試験があります。この目的のために、認定試験のAssociate-Data-Practitionerトレーニング資料では、問題解決スキルを要約し、一般的なテンプレートを紹介しています。ユーザーは、提供された回答テンプレートに基づいて回答をスカウトし、スコアをスカウトできます。そのため、ユニバーサルテンプレートは、ユーザーがAssociate-Data-Practitioner試験を勉強して合格するための貴重な時間を大幅に節約できます。

Associate-Data-Practitionerテキスト: https://jp.fast2test.com/Associate-Data-Practitioner-premium-file.html

Carl Bell Carl Bell

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Google Associate-Data-Practitioner資格勉強、Associate-Data-Practitionerテキスト

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

完璧-高品質なAssociate-Data-Practitioner資格勉強試験-試験の準備方法Associate-Data-Practitionerテキスト

Google Cloud Associate Data Practitioner 認定 Associate-Data-Practitioner 試験問題 (Q66-Q71):

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