GCP-DE Exams Training, GCP-DE Latest Study Guide

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 2: Designing data processing systems	20%	- Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 4: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing

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Google Data Engineer Sample Questions (Q49-Q54):

NEW QUESTION # 49
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B. Create an API using App Engine to receive and send messages to the applications
C. Create a table on Cloud Spanner, and insert and delete rows with the job information
D. Create a table on Cloud SQL, and insert and delete rows with the job information

Answer: A

NEW QUESTION # 50
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 time
C. Trigger that is a combination of other triggers
D. Trigger based on element size in bytes

Answer: D

Explanation:
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 Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 51
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Composer
B. Cloud Dataflow
C. Cloud Scheduler
D. Cloud Functions

Answer: C

NEW QUESTION # 52
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. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Answer: C

NEW QUESTION # 53
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
C. Assemble 1,000 files into a tape archive (TAR) fil
D. Introduce data compression for each file to increase the rate file of file transfer.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
F. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Answer: A,F

NEW QUESTION # 54
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