Fast2test Professional-Data-Engineer問題集でリアル試験問題でテストエンジン問題集でトレーニング
Google Professional-Data-Engineerテスト問題集とオンライン試験エンジン
Google Professional-Data-Engineer試験に備えるために、候補者はオンラインで利用可能な多くのリソースを利用することができます。Googleは、オンラインコース、練習問題、勉強ガイドなど、さまざまなトレーニングコースと学習資料を提供しています。さらに、多くの勉強グループやフォーラムがあり、試験に備えている他の専門家とつながることができます。適切な準備と献身により、専門家はGoogle認定プロフェッショナルデータエンジニアの認定を取得し、Google Cloud Platformの専門知識を証明することができます。
Google Professional-Data-Engineer試験は、Google Cloud Platformで作業するプロフェッショナルのスキルと知識をテストするために設計されています。データ処理システムの設計、構築、管理の経験があり、機械学習モデルやデータ分析ツールを扱う人々に向けたものです。この試験は、ビジネスのニーズに応えるためにGoogle Cloud Platformツールを使用してソリューションを開発・実装する能力をプロフェッショナルが持っているかどうかをテストするために設計されています。
質問 # 67
Which software libraries are supported by Cloud Machine Learning Engine?
- A. TensorFlow and Torch
- B. Theano and TensorFlow
- C. Theano and Torch
- D. TensorFlow
正解：D
解説：
Explanation
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does
質問 # 68
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
- A. Recurrent neural network
- B. Linear regression
- C. Feedforward neural network
- D. Logistic classification
正解：B
解説：
Forecasting and Liner regression is used for predicting housing price.
質問 # 69
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for
sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows
your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube
channels log data. How should you set up the log data transfer into Google Cloud?
- A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-
Regional storage bucket as a final destination. - B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as
a final destination. - C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional
storage bucket as a final destination. - D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
storage bucket as a final destination.
正解：B
質問 # 70
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. Use at least 300 GB of data.
- B. Run your test for at least 10 minutes.
- C. Do not use a production instance.
- D. Before you test, run a heavy pre-test for several minutes.
正解：C
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.
Reference: https://cloud.google.com/bigtable/docs/performance
質問 # 71
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. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
- B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
- C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
- D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
正解：A
質問 # 72
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?
- A. Drain
- B. Stop
- C. Finish
- D. Cancel
正解：A
解説：
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow
service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
質問 # 73
The Dataflow SDKs have been recently transitioned into which Apache service?
- A. Apache Beam
- B. Apache Kafka
- C. Apache Spark
- D. Apache Hadoop
正解：A
解説：
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive
https://cloud.google.com/dataflow/docs/
質問 # 74
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?
- A. Use a higher-memory node so that the job runs faster
- B. Use SSDs on the worker nodes so that the job can run faster
- C. Migrate the workload to Google Cloud Dataflow
- D. Use pre-emptible virtual machines (VMs) for the cluster
正解：C
解説：
Explanation
質問 # 75
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
- A. Use Cloud ML Engine for training existing Spark ML models
- B. Rewrite your models on TensorFlow, and start using Cloud ML Engine
- C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
- D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
正解：A
解説：
Explanation
質問 # 76
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Use .fromQueryoperation to read specific fields from the table.
- B. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.
- C. Specify the TableReferenceobject in the code.
- D. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
正解：B
質問 # 77
You need to compose visualizations for operations teams with the following requirements:
Which approach meets the requirements?
- A. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
- B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
- C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
- D. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
正解：C
質問 # 78
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
- A. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
- B. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
- C. They have not assigned the timestamp, which causes the job to fail
- D. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
正解：B
質問 # 79
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log dat a. How should you set up the log data transfer into Google Cloud?
- A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
- B. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
- C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
- D. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
正解：C
解説：
storage bucket as a final destination.
質問 # 80
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
- A. Train on the new data while using the existing data as your test set.
- B. Continuously retrain the model on a combination of existing data and the new data.
- C. Continuously retrain the model on just the new data.
- D. Train on the existing data while using the new data as your test set.
正解：B
質問 # 81
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?
- A. LIMIT
- B. SELECT
- C. WHERE
- D. BETWEEN
正解：B
解説：
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by
BigQuery.
質問 # 82
Your company maintains a hybrid deployment with GCP, where analytics are performed on your
anonymized customer data. The data are imported to Cloud Storage from your data center through parallel
uploads to a data transfer server running on GCP. Management informs you that the daily transfers take
too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action
should you take?
- A. Increase your network bandwidth from your datacenter to GCP.
- B. Increase the size of the Google Persistent Disk on your server.
- C. Increase the CPU size on your server.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
正解：A
解説：
Explanation/Reference:
質問 # 83
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
- A. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
- B. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
- C. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
- D. Increase the size of your parquet files to ensure them to be 1 GB minimum.
正解：D
質問 # 84
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