GCP-DE認定資格試験問題集 & GCP-DE日本語版テキスト内容

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Storing and managing data	20%	- Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
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
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration - Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance
Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows - Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic

>> GCP-DE認定資格試験問題集 <<

信頼できるGCP-DE認定資格試験問題集 | 最初の試行で簡単に勉強して試験に合格する & 一番いいGCP-DE: Data Engineer

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Google Data Engineer 認定 GCP-DE 試験問題 (Q70-Q75):

質問 # 70
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. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
C. Mount the Hive tables from HDFS.
D. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
E. Replicate external Hive tables to the native ones.
F. Mount the Hive tables locally.
G. Mount the Hive tables locally.
H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
I. Then run the Hadoop utility to copy them do HDF
J. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
K. Replicate external Hive tables to the native ones.
L. Load the ORC files into BigQuer

正解：B、G

質問 # 71
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D. Create a cron schedule in Cloud Dataprep.

正解：A

質問 # 72
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. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
C. Set a sliding time window of 1 hour every 5 minute
D. If that number falls below 4000, send an alert.
E. Consume the stream of data in Cloud Dataflow using Kafka I
F. If that number falls below 4000, send an alert.
G. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
H. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
J. 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.
K. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
L. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
M. Consume the stream of data in Cloud Dataflow using Kafka I

正解：H

質問 # 73
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use K-means Clustering to detect faces in the pixels.

正解：A

解説：
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

質問 # 74
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Read and write to Google Cloud Storage; write to Google Cloud Logging
C. Execute to Google Cloud Storage; execute to Google Cloud Logging
D. Execute to Google Cloud Storage; write to Google Cloud Logging

正解：B

解説：
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

質問 # 75
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GCP-DE日本語版テキスト内容: https://www.jpshiken.com/GCP-DE_shiken.html