VALID PROFESSIONAL-DATA-ENGINEER TEST LABS, RELIABLE PROFESSIONAL-DATA-ENGINEER BRAINDUMPS BOOK

Valid Professional-Data-Engineer Test Labs, Reliable Professional-Data-Engineer Braindumps Book

Valid Professional-Data-Engineer Test Labs, Reliable Professional-Data-Engineer Braindumps Book

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Google Professional Data Engineer exam covers a wide range of topics, including the understanding of the Google Cloud Platform for storing, processing, and analyzing data, designing data processing systems, data modeling, data security, and compliance. Additionally, the exam tests the candidate's knowledge of implementing data pipelines, data transformation and processing, and machine learning models on the Google Cloud Platform. Passing Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge required to design and build data processing systems that meet business requirements and scale efficiently on the Google Cloud Platform.

How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

  • Design data processing systems
  • Ensure solution quality
  • Operationalize machine learning models
  • Build and operationalize data processing systems

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

Google Professional-Data-Engineer Certification Exam is a comprehensive test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that assess the candidates' knowledge of various Google Cloud Platform services, including BigQuery, Cloud Spanner, Cloud Dataflow, and Cloud Dataproc. Additionally, the exam evaluates the candidates' ability to design data processing systems that are scalable, reliable, and secure.

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Google Certified Professional Data Engineer Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

  • A. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
  • B. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
  • C. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
  • D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Answer: A

Explanation:
Import and Export to Bigquery from Cloud Storage is FREE. Also, when u store the csv files, Cloud Storage is cheaper than Bigquery. For processing Dataproc is cheaper than Dataflow.


NEW QUESTION # 120
You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuory. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?

  • A. Create a VPC Service Controls perimeter that contains the VPC network and add Dataflow. Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.
  • B. Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
  • C. Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
  • D. Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.

Answer: D

Explanation:
To deploy a batch pipeline in Dataflow that adheres to the organizational constraint of using only internal IP addresses, ensuring Private Google Access is the most effective solution. Here's why option D is the best choice:
Private Google Access:
Private Google Access allows resources in a VPC network that do not have external IP addresses to access Google APIs and services through internal IP addresses.
This ensures compliance with the organizational constraint of using only internal IPs while allowing Dataflow to access Cloud Storage and BigQuery.
Dataflow with Internal IPs:
Dataflow can be configured to use only internal IP addresses for its worker nodes, ensuring that no external IP addresses are assigned.
This configuration ensures secure and compliant communication between Dataflow, Cloud Storage, and BigQuery.
Firewall and Network Configuration:
Enabling Private Google Access requires ensuring the correct firewall rules and network configurations to allow internal traffic to Google Cloud services.
Steps to Implement:
Enable Private Google Access:
Enable Private Google Access on the subnetwork used by the Dataflow pipeline gcloud compute networks subnets update [SUBNET_NAME]
--region [REGION]
--enable-private-ip-google-access
Configure Dataflow:
Configure the Dataflow job to use only internal IP addresses
gcloud dataflow jobs run [JOB_NAME]
--region [REGION]
--network [VPC_NETWORK]
--subnetwork [SUBNETWORK]
--no-use-public-ips
Verify Access:
Ensure that firewall rules allow the necessary traffic from the Dataflow workers to Cloud Storage and BigQuery using internal IPs.
Reference:
Private Google Access Documentation
Configuring Dataflow to Use Internal IPs
VPC Firewall Rules


NEW QUESTION # 121
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Deploy small Kafka clusters in your data centers to buffer events.
  • B. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Establish a Cloud Interconnect between all remote data centers and Google.
  • D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: D


NEW QUESTION # 122
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

  • A. Weights
  • B. Biases
  • C. Input values
  • D. Continuous features

Answer: A,B

Explanation:
Explanation
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference:
https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground


NEW QUESTION # 123
What are two of the benefits of using denormalized data structures in BigQuery?

  • A. Reduces the amount of data processed, reduces the amount of storage required
  • B. Increases query speed, makes queries simpler
  • C. Reduces the amount of data processed, increases query speed
  • D. Reduces the amount of storage required, increases query speed

Answer: B

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses. Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


NEW QUESTION # 124
......

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