Google Professional-Data-Engineer復習資料、Professional-Data-Engineer過去問題

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Google Professional-Data-Engineer認定試験に合格することは、複数のキャリア機会につながる重要な業績です。これにより、個人はデータエンジニアリングの専門家として自己を確立し、複雑なデータ処理システムを設計、実装、管理する能力を示すことができます。さらに、認定データエンジニアの需要が高まっているため、信頼性と収益の可能性も高まります。したがって、データエンジニアリングのキャリアを確立するためには、Google Professional-Data-Engineer認定を取得することが必要不可欠です。

Google Professional-Data-Engineerの試験は、データエンジニアリングの概念や技術に深い理解が必要な、厳しい総合テストです。候補者はGoogle Cloud Platform、Hadoop、Spark、SQLなどの様々なツールやプラットフォームに精通している必要があります。この試験に合格することで、候補者は雇用主や同僚に対して、大量のデータを扱い、組織の特定のニーズに合わせた複雑なデータ処理システムを設計・構築するための知識とスキルを持っていることを証明できます。

Google Professional-Data-Engineer認定は、業界で非常に高く評価されています。保持者がGoogle Cloud Platform上でデータソリューションを設計および実装するスキルと知識を持っていることを示します。認定は、特にBig Dataに取り組むことを目指す人にとって重要であり、Google Cloud Platformは主要なBig Dataソリューションのプロバイダーの1つです。

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q177-Q182):

質問 # 177
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have different features from the mutated samples in the database.
B. There are very few occurrences of mutations relative to normal samples.
C. There are roughly equal occurrences of both normal and mutated samples in the database.
D. You already have labels for which samples are mutated and which are normal in the database.
E. You expect future mutations to have similar features to the mutated samples in the database.

正解：B、E

解説：
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection

質問 # 178
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?

A. BigQuery and Cloud Storage
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. Cloud Datastore and Cloud Bigtable

正解：C

質問 # 179
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
- 8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
- 3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
- 60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
* Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
* 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Dataflow, Cloud SQL, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
E. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

正解：D

解説：
Explanation

質問 # 180
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country
You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
B. The [myproject:mydataset.mytable] table has too many partitions
C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many
NULL values
D. Users are running too many concurrent queries in the system

正解：D

質問 # 181
You are building an application to share financial market data with consumers, who will receive data feeds.
Data is collected from the markets in real time. Consumers will receive the data in the following ways:
* Real-time event stream
* ANSI SQL access to real-time stream and historical data
* Batch historical exports
Which solution should you use?

A. Cloud Pub/Sub, Cloud Storage, BigQuery
B. Cloud Dataflow, Cloud SQL, Cloud Spanner
C. Cloud Dataproc, Cloud Dataflow, BigQuery
D. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

正解：A

質問 # 182
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私たちは皆、ほとんどの候補者が製品の品質を心配することを知っていました。Professional-Data-Engineer学習教材の品質を保証するために、会社のすべての労働者は、共通の目標のために、 ; Professional-Data-Engineer試験問題です。 Professional-Data-Engineerガイドトレントを購入すると、高品質の製品、リーズナブルな価格、アフターサービスを提供することが保証されます。私たちのProfessional-Data-Engineerテストトレントは、他の学習教材よりもあなたにとってより良い選択だと思います。

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