GCP-DE資格講座 & GCP-DE資格復習テキスト

GCP-DE証明書を所有して、自分が有能であることを証明し、特定の分野で優れた実用的な能力を高めることができます。したがって、Xhs1991あなたは有能な人々とみなされ、尊敬されます。テストGCP-DE認定に合格すると、目標を実現するのに役立ちます。また、GCP-DEガイドトレントを購入すると、GCP-DE試験に簡単に合格できます。 GCP-DE試験問題は最も専門的な専門家によって書かれているため、GCP-DE学習教材の品質は素晴らしいです。そして、試験に合格するために、Data Engineer学習ガイドを常に最新の状態に保ちます。

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Applying data processing logic 3. Optimizing transformations for performance - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Topic 2: Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
Topic 3: Designing data processing systems	24%	- Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 3. Selecting appropriate storage solutions - Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing
Topic 4: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
Topic 5: Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns

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Google GCP-DE資格復習テキスト & GCP-DE関連試験

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Google Data Engineer 認定 GCP-DE 試験問題 (Q46-Q51):

質問 # 46
MJTelco is building a custom interface to share dat
a. 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. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage

正解：C

質問 # 47
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Combine highly co-dependent features into one representative feature.
B. Remove the features that have null values for more than 50% of the training records.
C. Eliminate features that are highly correlated to the output labels.
D. Instead of feeding in each feature individually, average their values in batches of 3.

正解：A

質問 # 48
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Set a sliding time window of 1 hour every 5 minute
B. If that number falls below 4000, send an alert.
C. Consume the stream of data in Cloud Dataflow using Kafka I
D. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
E. If that number falls below 4000, send an alert.
F. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
G. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
H. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
I. Consume the stream of data in Cloud Dataflow using Kafka I
J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
L. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer

正解：G

質問 # 49
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Mount the Hive tables from HDFS.
B. Replicate external Hive tables to the native ones.
C. Mount the Hive tables locally.
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
E. Then run the Hadoop utility to copy them do HDF
F. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
G. Replicate external Hive tables to the native ones.
H. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
I. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
J. Load the ORC files into BigQuer
K. Mount the Hive tables locally.
L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF

正解：I、K

質問 # 50
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Dialogflow Enterprise Edition
B. Cloud Natural Language API
C. Cloud Speech-to-Text API
D. Cloud AutoML Natural Language

正解：D

質問 # 51
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