ADP日本語版問題集 & ADP日本語版参考資料

IT業の多くの人がいくつか認証試験にパスしたくて、それなりの合格証明書が君に最大な上昇空間を与えます。この競争の激しい業界でとんとん拍子に出世させるのはGoogleのADP認定試験ですが、簡単にパスではありません。でもたくさんの方法があって、最も少ない時間をエネルギーをかかるのは最高です。

Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Topic 2: Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. BigQuery SQL usage 2. Data aggregation and transformation
Topic 3: Data Management	25%	- Data governance and security 1. Data lifecycle management 2. IAM and access control - Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies
Topic 4: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)

>> ADP日本語版問題集 <<

ADP日本語版参考資料、ADPテスト参考書

ADP問題集の品質を確かめ、この問題集はあなたに合うかどうかを確認することができるように、TopexamはADP問題集の一部のダウンロードを無料で提供します。二つのバージョンのどちらでもダウンロードできますから、Topexamのサイトで検索してダウンロードすることができます。体験してから購入するかどうかを決めてください。そうすると、ADP問題集の品質を知らないままに問題集を購入してから後悔になることを避けることができます。

Google Associate Data Practitioner 認定 ADP 試験問題 (Q27-Q32):

質問 # 27
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. 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.
B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
C. Create a view that filters out rows that are older than one year.
D. Require users to specify a partition filter using the alter table statement in SQL.

正解：B

質問 # 28
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

正解：B

質問 # 29
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Nearline storage.
B. Store the data in Cloud Storage using Coldline storage.
C. Store the data in Cloud Storage using Standard storage.
D. Store the data in Cloud Storage using Archive storage.

正解：A

質問 # 30
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
B. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

正解：A

質問 # 31
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 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.
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 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
D. 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.

正解：B

質問 # 32
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ADP日本語版参考資料: https://www.topexam.jp/ADP_shiken.html