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Passing the Google Professional-Cloud-Architect Exam is a great way to demonstrate your expertise in using GCP to design and manage cloud solutions. With this certification, you can show potential employers that you have the skills needed to design and implement complex cloud architectures using GCP. Google Certified Professional - Cloud Architect (GCP) certification can also lead to new career opportunities and higher salaries.

Section #5. Managing implementation

This section has two major topics and assesses the candidates’ skills in application development, system migration, data management, API development/usage best practices, and testing frameworks. Interacting with Google Cloud programmatically is the key focus of the second sub-topic. The main considerations here are Cloud Emulators, Google Cloud Shell, and Cloud SDK.

Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q246-Q251):

NEW QUESTION # 246
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available
24hrs a day. Your business analysts have experience only with using a SQL interface.
How should you store the data to optimize it for ease of analysis?

A. Stream data into Google Cloud Datastore
B. Load data into Google BigQuery
C. Put flat files into Google Cloud Storage
D. Insert data into Google Cloud SQL

Answer: B

Explanation:
Explanation/Reference:
Explanation:
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
References: https://cloud.google.com/bigquery/

NEW QUESTION # 247
One of the developers on your team deployed their application in Google Container Engine with the Dockerfile below. They report that their application deployments are taking too long.

You want to optimize this Dockerfile for faster deployment times without adversely affecting the app's functionality.
Which two actions should you take? Choose 2 answers.

A. Use a slimmed-down base image like Alpine Linux
B. Copy the source after he package dependencies (Python and pip) are installed
C. Use larger machine types for your Google Container Engine node pools
D. Remove dependencies from requirements.txt
E. Remove Python after running pip

Answer: A,B

Explanation:
Explanation/Reference:
Explanation:
The speed of deployment can be changed by limiting the size of the uploaded app, limiting the complexity of the build necessary in the Dockerfile, if present, and by ensuring a fast and reliable internet connection.
Note: Alpine Linux is built around musl libc and busybox. This makes it smaller and more resource efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux environment but a large selection of packages from the repository.
References: https://groups.google.com/forum/#!topic/google-appengine/hZMEkmmObDU
https://www.alpinelinux.org/about/

NEW QUESTION # 248
For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US.
Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

A. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
B. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
C. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
D. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a regional bucket and use a Cloud Dataproc cluster .....

Answer: D

NEW QUESTION # 249
You deploy your custom java application to google app engine.
It fails to deploy and gives you the following stack trace:

A. Digitally sign all of your JAR files and redeploy your application.
B. Upload missing JAR files and redeploy your application
C. Recompile the CLoakedServlet class using and MD5 hash instead of SHA1

Answer: A

NEW QUESTION # 250
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore.
What should you do?

A. Point gcloud datastore create-indexes to your configuration file
B. Create an HTTP request to the built-in python module to send the index configuration file to your application
C. Upload the configuration file the App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes
D. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file

Answer: A

Explanation:
Explanation
https://cloud.google.com/datastore/docs/tools/indexconfig#Datastore_Updating_indexes

NEW QUESTION # 251
......

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