GCP-DE問題と解答、GCP-DE受験対策

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ingesting and processing data25%- Building data pipelines
  • 1. Developing batch and streaming workflows
  • 2. Handling data quality and consistency
  • 3. Implementing ingestion mechanisms
- Transforming data
  • 1. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution
Topic 2: Designing data processing systems24%- 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. Planning for data migration and integration
  • 2. Designing for data transformation and enrichment
  • 3. Defining architecture for batch and streaming processing
Topic 3: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Implementing data sharing and governance
  • 2. Preparing datasets for querying and reporting
  • 3. Optimizing query performance
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
Topic 4: Storing and managing data20%- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Designing data warehouses and data lakes
  • 3. Using data storage services appropriately
- Optimizing storage performance and cost
  • 1. Implementing cost optimization strategies
  • 2. Configuring storage for access patterns
Topic 5: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Monitoring and troubleshooting pipelines
  • 2. Ensuring reliability and recoverability
  • 3. Orchestrating and scheduling jobs
- Automation and optimization
  • 1. Automating deployment and management
  • 2. Optimizing resource usage and costs

>> GCP-DE問題と解答 <<

効率的GCP-DE｜最高のGCP-DE問題と解答試験｜試験の準備方法Data Engineer受験対策

多くの人は、GCP-DEの有効な学習ガイド教材を購入することを好みます。なぜなら、購入するだけで間違いなくテストに合格できると深く信じているからです。 GCP-DEガイドの質問を好む理由は、教材の品質が非常に高いためです。長年、私たちは常にGCP-DE学習教材の完成に専念しています。一流の研究チームと一流の販売サービスを強化します。 GCP-DEガイドの質問とGCP-DE学習教材の更新を担当する専門スタッフの研究と制作を専門とするエキスパートチームを後押しします。

Google Data Engineer 認定 GCP-DE 試験問題 (Q76-Q81):

質問 # 76
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

正解：B

解説：
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do


質問 # 77
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

正解：A、D

解説：
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


質問 # 78
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

正解：B、C、D

解説：
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


質問 # 79
Cloud Dataproc is a managed Apache Hadoop and Apache service.

正解：A

解説：
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/


質問 # 80
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

正解：B


質問 # 81
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