GCP-DE受験対策、GCP-DE過去問無料

GCP-DE試験はGoogleの認定試験の一つですが、もっとも重要なひとつです。GoogleのGCP-DEの認定試験に合格するのは簡単ではなくて、Fast2testはGCP-DE試験の受験生がストレスを軽減し、エネルギーと時間を節約するために専門研究手段として多様な訓練を開発して、Fast2testから君に合ったツールを選択してください。

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability
Designing data processing systems	20%	- Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection
Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing
Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. IAM and access control 2. Data encryption and compliance

>> GCP-DE受験対策 <<

Google GCP-DE過去問無料 & GCP-DE受験方法

Fast2testのGoogleのGCP-DE「Data Engineer」トレーニング資料を利用したら、初めて試験を受けるあなたでも一回で試験に合格できることを保証します。Fast2testのGoogleのGCP-DEトレーニング資料を利用しても合格しないのなら、我々は全額で返金することができます。あなたに他の同じ値段の製品を無料に送って差し上げます。

Google Data Engineer 認定 GCP-DE 試験問題 (Q17-Q22):

質問 # 17
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Remove the features that have null values for more than 50% of the training records.
B. Combine highly co-dependent features into one representative feature.
C. Instead of feeding in each feature individually, average their values in batches of 3.
D. Eliminate features that are highly correlated to the output labels.

正解：B

質問 # 18
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

正解：A

質問 # 19
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 "person.city".
B. Add ", UNNEST(person)" before the WHERE clause.
C. Change "person" to "city.person".
D. Add ", UNNEST(city)" before the WHERE clause.

正解：B

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 # 20
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Execute to Google Cloud Storage; write to Google Cloud Logging
C. Execute to Google Cloud Storage; execute to Google Cloud Logging
D. Read and write to Google Cloud Storage; write to Google Cloud Logging

正解：D

解説：
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

質問 # 21
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

正解：B

解説：
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

質問 # 22
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大量の時間と金銭をかかるのに比べて、正しい仕方は肝心なことです。もしあなたはGoogle GCP-DE試験に準備しているなら、あんたのための整理される備考資料はあなたにとって最善のオプションです。我々の目標はあなたに試験にうまく合格させることです。弊社の誠意を信じてもらいたいし、Google GCP-DE試験2成功するのを祈って願います。

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