Google Professional-Data-Engineer日本語学習内容、Professional-Data-Engineerテスト内容

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Google Professional-Data-Engineer認定は、Google Cloud Technologiesを使用してデータパイプラインを開発および管理する際の専門知識を実証したいデータエンジニアに最適です。この認定は、データエンジニアリングの分野でキャリアの見通しを強化したい個人にも適しています。試験に合格することにより、候補者は、Google Cloud Servicesを使用してデータ処理システムの設計、構築、維持に習熟することを証明できます。

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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 Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q218-Q223):

質問 # 218
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Access historical data by using time travel in BigQuery.
B. Create a BigQuery table snapshot on a daily basis.
C. Export the data from BigQuery into a new table that excludes the corrupted data.
D. Migrate your data to multi-region BigQuery buckets.

正解：A

解説：
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
Reference:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps

質問 # 219
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

正解：A

質問 # 220
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [0, 0, 0, 1, 0, 0, 1]
B. [0, 1]
C. [0, 5, 0, 0, 0, 0]
D. [1, 0, 0, 0, 0, 0, 0]

正解：B、D

解説：
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and
1s.
Reference:
https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

質問 # 221
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
* The user profile: What the user likes and doesn't like to eat
* The user account information: Name, address, preferred meal times
* The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

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

正解：C

質問 # 222
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger based on element size in bytes
C. Trigger based on time
D. Trigger that is a combination of other triggers

正解：B

解説：
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way

質問 # 223
......

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