試験の準備方法-実用的なGCP-DE試験解答試験-権威のあるGCP-DE試験

GCP-DEトレーニングテストの購入は複雑ではありません。Google主に4つのステップがあります。最初に、必要に応じて対応するバージョンを選択できます。次に、正しいメールアドレスを入力する必要があります。また、その後のリリースでユーザーがメールを変更した場合は、CertShikenメールを更新する必要があります。次に、ユーザーは購入するためにGCP-DE学習教材の支払いページに入る必要があります。最後に、支払いから10分以内に、システムは自動的にData EngineerのGCP-DE学習資料をユーザーのメールアドレスに送信します。そして、すぐにGCP-DE試験に合格して合格することができます。

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Manage logging and alerting 2. Implement fault tolerance and recovery 3. Monitor data pipelines and workloads - Building data processing systems 1. Implement streaming data systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement batch processing systems
Designing data processing systems	22%-27%	- Designing data pipelines 1. Optimize pipeline performance and cost 2. Build ETL and ELT pipelines 3. Design orchestration workflows - Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems
Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Protect sensitive data 3. Manage IAM and access control - Data quality management 1. Maintain metadata and lineage 2. Validate and clean data 3. Implement data governance
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Tune query and pipeline performance 2. Optimize storage and compute usage 3. Control operational costs - Reliability and scalability 1. Implement disaster recovery strategies 2. Scale data workloads efficiently 3. Design highly available systems

>> GCP-DE試験解答 <<

試験の準備方法-高品質なGCP-DE試験解答試験-更新するGCP-DE試験

GoogleのGCP-DE認定試験を受けてGCP-DE認証資格を取得したいですか。CertShikenはあなたの成功を保証することができます。もちろん、試験の準備をするときに試験に関連する知識を学ぶのは必要です。なお大切なのは、自分に相応しい効率的なツールを選択することです。CertShikenのGCP-DE問題集はあなたに合う最善の勉強法です。この高品質の問題集は信じられないほどの結果を見せることができます。自分が試験に合格できない心配があれば、はやくCertShikenのウェブサイトをクリックしてもっと多くの情報を読んでください。

Google Data Engineer 認定 GCP-DE 試験問題 (Q41-Q46):

質問 # 41
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataflow to run your transformation
B. Use the diagnose command to generate an operational output archiv
C. Resize the number of worker nodes in your cluster via the command line.
D. Use Cloud Dataflow to run your transformation
E. Monitor the total execution time for a sampling of job
F. Locate the bottleneck and adjust cluster resources.
G. Configure the job to use non-default Compute Engine machine types when needed.
H. Use Cloud Dataproc to run your transformation
I. Monitor the job system lag with Stackdrive
J. Use the default autoscaling setting for worker instances.
K. Monitor CPU utilization for the cluste
L. Use Cloud Dataproc to run your transformation

正解：K

質問 # 42
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

正解：D

解説：
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

質問 # 43
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
C. Create a temporary table in Cloud Spanner that will act as a buffer for new data
D. Use a larger instance type for your Cloud Dataflow workers
E. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G. Increase the number of max workers

正解：D、E

質問 # 44
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. Eliminate features that are highly correlated to the output labels.
C. Combine highly co-dependent features into one representative feature.
D. Instead of feeding in each feature individually, average their values in batches of 3.

正解：C

質問 # 45
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
C. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Merge the transformed tables together with a SQL query

正解：D

質問 # 46
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GCP-DE認定資格を取得して認定資格を取得すると、将来の雇用と開発がある程度決まるため、GCP-DE試験ガイドは競争力のある従業員になるために尽力しています。家に戻っても問題ありません。実際、GCP-DE試験に合格するための最良の方法は近視であるとGCP-DEのテスト準備を考えてください。彼らはこれを達成できるだけでなく、より多くのコンテンツを同時に覚えることができます。

GCP-DE試験: https://www.certshiken.com/GCP-DE-shiken.html