真実的なGCP-DE受験記 &合格スムーズGCP-DE関連資格知識 |効果的なGCP-DE試験勉強書

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Orchestrating and scheduling jobs 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management
Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows - Transforming data 1. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution
Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Designing for data transformation and enrichment 2. Planning for data migration and integration 3. Defining architecture for batch and streaming processing
Storing and managing data	20%	- Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance

>> GCP-DE受験記 <<

Google GCP-DE関連資格知識 & GCP-DE試験勉強書

JapancertのGoogleのGCP-DE試験資料は同じシラバスに従って研究されたのです。それに、資料もずっとアップグレードしていますから、実際の試験問題とよく似ています。Japancertの試験合格率も非常に高いことは否定することができない事実です。JapancertのGoogleのGCP-DE試験トレーニング資料の値段は手頃で、IT認証の受験生のみなさんによく適用します。

Google Data Engineer 認定 GCP-DE 試験問題 (Q33-Q38):

質問 # 33
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud SQL

正解：C

質問 # 34
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
D. Create an application that provides an AP
E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
F. Create an application that writes to a Cloud SQL database to store the dat

正解：D

質問 # 35
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
B. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D. Use BigQuery to export the data for the table to a CSV fil
E. Add a new column called FullName to the Users tabl
F. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.

正解：C

質問 # 36
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "city.person".
B. Change "person" to "person.city".
C. Add ", UNNEST(person)" before the WHERE clause.
D. Add ", UNNEST(city)" before the WHERE clause.

正解：C

解説：
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

質問 # 37
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. Python using MapReduce
B. Java using MapReduce
C. HiveQL using Hive
D. PigLatin using Pig

正解：A

質問 # 38
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無料のアップデートとオンラインカスタマーサービスを提供します。これは終日回線で機能します。 GCP-DE学習教材は、GCP-DE学習教材のさまざまなバージョンを提供し、GCP-DE学習者は時間と労力をほとんどかけずに選択できます。 GCP-DE試験準備は、購入後すぐに使用できます。GCP-DE試験の質問は5〜10分以内に送信されます。私たちはあなたの時間をあなたが見ているように貴重な自分の時間として扱いますので、無駄なプロセスで1、2分を無駄にすることはありません。使用はご安心ください。GCP-DE試験に必ず合格すると信じています。

GCP-DE関連資格知識: https://www.japancert.com/GCP-DE.html