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IT技術の発展に従って、Professional-Data-Engineer試験資格認定証明書を持つ人はますます多くなっていました。どんなProfessional-Data-Engineer試験参考書を選びますか？ここで、お勧めたいのは弊社のProfessional-Data-Engineer試験参考書です。Professional-Data-Engineer試験参考書の内容は全面的で、わかりやすいです。そのほかに、Professional-Data-Engineer試験の合格率は高い、多くの受験者が試験に合格しました。だから、弊社のProfessional-Data-Engineer試験参考書はいろいろな資料の中で目立っています。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality | 28% | - Security and governance
|
| Topic 2: Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Topic 3: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Topic 4: Designing data processing systems | 22% | - Data architecture and storage design
|
>> Professional-Data-Engineer合格問題 <<
今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。Professional-Data-Engineer認定試験はGoogleの中に重要な認証試験の一つですが、ShikenPASSにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってGoogle Professional-Data-Engineer「Google Certified Professional Data Engineer Exam」認証試験に参加する方に対して問題集を研究続けています。
質問 # 399
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.
We also need environments in which our data scientists can carefully study and quickly adapt our models.
Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
正解：D
質問 # 400
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
正解：C
質問 # 401
Which of these sources can you not load data into BigQuery from?
正解：B
解説：
Explanation
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data
質問 # 402
Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?
正解：B
解説：
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi-region_configu
質問 # 403
You are working on a sensitive project involving private user data. You have set up a project on Google
Cloud Platform to house your work internally. An external consultant is going to assist with coding a
complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain
users' privacy?
正解：A
質問 # 404
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