Professional-Data-Engineer必殺問題集、Professional-Data-Engineer学習資料

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Professional-Data-Engineer必殺問題集, Professional-Data-Engineer学習資料, Professional-Data-Engineer模擬対策, Professional-Data-Engineer日本語認定, Professional-Data-Engineer難易度

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クライアントは購入前にProfessional-Data-Engineerトレーニング資料を自由に試してダウンロードして、製品を理解し、購入するかどうかを決定できます。製品のウェブサイトページには、Professional-Data-Engineer学習に関する質問の詳細が記載されています。テストバンクから選択されたすべてのタイトルの一部であるデモと質問と回答の形式を確認し、教材のWebサイトページでソフトウェアの形式を知ることができます。

Google Professional-Data-Engineer Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Building and operationalizing data processing systems 25% - Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Ingesting data from various sources
  • 3. Orchestrating data workflows
Topic 2: Designing data processing systems 20% - Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability and fault tolerance
Topic 3: Maintaining and automating data workloads 18% - Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
Topic 4: Ensuring solution quality and reliability 17% - Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
Topic 5: Operationalizing machine learning models 20% - Deploying and maintaining ML models
  • 1. Model serving and monitoring
  • 2. Optimizing model performance and cost
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Handling structured and unstructured data

>> Professional-Data-Engineer必殺問題集 <<

Professional-Data-Engineer学習資料 & Professional-Data-Engineer模擬対策

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q245-Q250):

質問 # 245
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

  • A. Execute gsutil rsyncfrom the on-premises servers.
  • B. Use Cloud Dataflow and write the data to Cloud Storage.
  • C. Write a job template in Cloud Dataproc to perform the data transfer.
  • D. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

正解：B


質問 # 246
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

  • A. Create and share a new dataset and table that contains the aggregate results.
  • B. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
  • C. Create and share an authorized view that provides the aggregate results.
  • D. Create and share a new dataset and view that provides the aggregate results.

正解：B

解説：
Explanation/Reference:
Reference: https://cloud.google.com/bigquery/docs/access-control


質問 # 247
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

  • A. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
  • B. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
  • C. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.
    Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
  • D. Change the data pipeline to use BigQuery for storing stock trades, and update your application.

正解：B


質問 # 248
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

  • A. Do not use a production instance.
  • B. Use at least 300 GB of data.
  • C. Run your test for at least 10 minutes.
  • D. Before you test, run a heavy pre-test for several minutes.

正解：A

解説：
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.


質問 # 249
You are collecting IoT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data, filtered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

  • A. Cluster table data by create_date, partition by location_id, and device_version.
  • B. Partition table data by create_date, location_id, and device_version.
  • C. Cluster table data by create_date, location_id, and device_version.
  • D. Partition table data by create_date, cluster table data by location_id, and device_version.

正解：D


質問 # 250
......

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