Google ADP

二十四時間のオンラインサービス

我が社の係員は心を込めて誠心誠意にお客様のあらゆる要求に答えします。いかなる場合でも、いかなる時間でも本社の係員に連絡し、ADPについての問題解決に力を入れて努力します。ご使用がわからない場合に、ヘルプが必要な場合に、遠慮なく私たちに連絡してください。

ADP試験問題集をすぐにダウンロード：成功に支払ってから、我々のシステムは自動的にメールであなたの購入した商品をあなたのメールアドレスにお送りいたします。（12時間以内で届かないなら、我々を連絡してください。Note：ゴミ箱の検査を忘れないでください。）

高い通過率

ADP模擬テストエンジンは繰り返しの練習であなたの解答能力を高めることができます。更に、本当な問題と正確の解答もADP勉強資料のメリットです。本社のADP勉強資料を使ったお客様の試験通過率は９８％に達し、採集したデータによると、ADP試験に参加したほとんどのお客様は合格しました。高い通過率こそ我が社は業界に一席を占める重要な保証です。

幸せは自分の心が決めます。あなたは自分の心に準じてADP試験に早く申し込みましょう。我々社は質高いADPトレーニング資料と行き届いたサービスを提供して、あなたはADP試験に合格するのを助けます。我々の商品を選んで、あなたは絶対後悔しないと信じられます。

お客様の需要に従って、わが社はADPトレーニング資料に三つのバージョンを作り上げました。一つはPDF版で、印刷できてADP練習問題を便利に閲覧しながらメモを取ります。二つはソフト版で、windowsシステムを搭載したパソコンに使用しなければいけない。パソコンにADP試験の実際環境を模擬して実行されます。ADP本番試験の雰囲気を体験できます。三つはオンライン版で、携帯やIPADなどの電子設備に使用することができる。あなたはいつでも、どこでも、ADPオンラインテストエンジンを使用して学習することができます。それは時間が余裕ではないお客様に対し大きなメリットです。

Google Associate Data Practitioner 認定 ADP 試験問題:

1. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create an object table.
B) Create a temporary table.
C) Create a native table.
D) Create an external table.

2. Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A) Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
B) Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
C) Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
D) Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

3. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
B) Create a view that filters out rows that are older than one year.
C) Require users to specify a partition filter using the alter table statement in SQL.
D) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

4. You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A) Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
B) Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
C) Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
D) Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

5. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D) Use Dataflow to transform the data and write the transformed data to BigQuery.

質問と回答：