ADP試験準備 & ADP日本語版対応参考書

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing and Analytics	20-30%	- Aggregate and summarize data - Build and maintain data pipelines - Apply statistical methods for analysis - Query and analyze datasets - Use BigQuery and SQL for analytics
Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Interpret and communicate findings - Build visualizations using Looker Studio - Present data insights to stakeholders - Create dashboards and reports
Data-Driven Decision Making	10-20%	- Identify stakeholders and requirements - Define success metrics - Assess data quality and completeness - Translate business requirements into data solutions
Data Preparation and Exploration	20-30%	- Ingest and acquire data - Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Identify data quality issues - Transform and prepare data for analysis

>> ADP試験準備 <<

ADP日本語版対応参考書 & ADP模擬練習

ADPトレーニング資料にはハラーン語は含まれておらず、すべてのページは献身的な熟練した専門家によって書かれています。当社のウェブサイトの専門家は、複雑な概念を簡素化し、例、シミュレーション、および図を追加して、理解しにくいかもしれないことを説明します。そのため、普通の試験官でも難なくすべての学習問題を習得できます。さらに、ADP受験者は、テストエンジンを使用することで自分自身に利益をもたらし、演習や回答などの多くのテスト問題を取得できます。

Google Associate Data Practitioner 認定 ADP 試験問題 (Q45-Q50):

質問 # 45
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 Dataflow to transform the data and write the transformed data to BigQuery.
C. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D. Use Cloud Run functions to transform and load the data into BigQuery.

正解：B

質問 # 46
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

正解：B

質問 # 47
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. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

正解：C

質問 # 48
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 Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B. 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.
C. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

正解：B

質問 # 49
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
B. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C. Create a custom quota for each analyst in BigQuery.
D. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

正解：D

質問 # 50
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現代社会では、私たちは毎日忙しいです。したがって、個々の時間は制限されています。事実、あなたが学ぶことを決心したなら、何もあなたを止めることはできません！幸運にも、ADP試験の資料に出会えます。 ADP試験の最短時間で改善できるようにお手伝いします。あなたもADP試験について何も知りません。全く問題ありません。約20〜30時間のガイダンスを受け入れるだけで、試験に簡単に参加できます。ご覧のとおり、ADP模擬試験はあまり時間をかけません。

ADP日本語版対応参考書: https://www.it-passports.com/ADP.html