Study Materials Google GCP-DE Review & GCP-DE Official Cert Guide

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. IAM and access control 2. Data encryption and compliance - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 2: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development
Topic 3: 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 4: 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

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GCP-DE Official Cert Guide - Valid GCP-DE Test Forum

The Google GCP-DE Certification is one of the most valuable certificates in the modern Google world. This Data Engineer (GCP-DE) certification exam is designed to validate a candidate's skills and knowledge level. With this GCP-DE exam everyone whether he is a beginner or seasoned professional can not only validate their expertise but also get solid proof of their skills and knowledge. By doing this you can gain several personal and professional benefits.

Google Data Engineer Sample Questions (Q10-Q15):

NEW QUESTION # 10
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
B. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
C. Use a larger instance type for your Cloud Dataflow workers
D. Create a temporary table in Cloud Spanner that will act as a buffer for new data
E. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
F. Increase the number of max workers
G. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

Answer: B,C

NEW QUESTION # 11
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E. Use Cloud Deployment Manager to automate access provision.
F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,D

NEW QUESTION # 12
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Answer: D

NEW QUESTION # 13
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
B. Create groups for your users and give those groups access to the dataset
C. Create a dummy user and grant dataset access to that use
D. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
E. Use the service account's private key to access the dataset
F. Create a service account and grant dataset access to that accoun

Answer: F

NEW QUESTION # 14
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 15
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