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Google Professional-Data-Engineer Exam Syllabus Topics:

Section Weight Objectives Ensuring solution quality and reliability 17% - Troubleshooting and optimization

1. Optimizing queries and workloads
2. Diagnosing performance issues

- Testing and validating data systems

1. Performance and scalability testing
2. Data quality validation

Maintaining and automating data workloads 18% - Automation and repeatability

1. Automating deployment and updates
2. Implementing CI/CD for data systems

- Resource optimization

1. Cost management and resource allocation
2. Choosing appropriate compute and storage options

Operationalizing machine learning models 20% - Deploying and maintaining ML models

1. Model serving and monitoring
2. Optimizing model performance and cost

- Preparing data for ML

1. Feature engineering and data preparation
2. Handling structured and unstructured data

Building and operationalizing data processing systems 25% - Deploying and managing systems

1. Monitoring and logging data processes
2. Managing infrastructure and resources

- Building data pipelines

1. Transforming and cleaning data
2. Ingesting data from various sources
3. Orchestrating data workflows

Designing data processing systems 20% - Designing for business requirements

1. Designing for scalability and elasticity
2. Selecting appropriate storage solutions
3. Designing for reliability and fault tolerance

- Designing for regulatory and security requirements

1. Ensuring data privacy and compliance
2. Implementing access control and data protection

>> Professional-Data-Engineer Antworten <<

Professional-Data-Engineer neuester Studienführer & Professional-Data-Engineer Training Torrent prep

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q412-Q417):

412. Frage

You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution.

What should you do?

A. Access historical data by using time travel in BigQuery.
B. Migrate your data to multi-region BigQuery buckets.
C. Create a BigQuery table snapshot on a daily basis.
D. Export the data from BigQuery into a new table that excludes the corrupted data.

Antwort: A

Begründung:

Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.

References:

Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps

413. Frage

You have an Apache Kafka cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins. What should you do?

A. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
B. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read from PubSub and write to GCS.
C. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
D. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read from PubSub and write to GCS.

Antwort: A

414. Frage

MJTelco Case Study

Company Overview

MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.

Company Background

Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.

Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.

Solution Concept

MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:

Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.

Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.

MJTelco will also use three separate operating environments - development/test, staging, and production

- to meet the needs of running experiments, deploying new features, and serving production customers.

Business Requirements

Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.

Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.

Technical Requirements

Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.

Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day

Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.

CEO Statement

Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.

CTO Statement

Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.

CFO Statement

The project is too large for us to maintain the hardware and software required for the data and analysis.

Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.

You need to compose visualization for operations teams with the following requirements:

Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once

every minute)

The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

A. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
B. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
C. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
D. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.

Antwort: A

415. Frage

Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

A. Add firewall rules in project A so only traffic from the VPC in project A is permitted.
B. Configure VPC Service Controls in the organization with a perimeter around project A.
C. Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.
D. Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.

Antwort: D

Begründung:

Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.

Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.

Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.

Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM | Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAM Documentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].

416. Frage

You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL. database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?

A. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream.Add those IP addresses to the firewall allowlist of your on-premises data center. Use IP Allowlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.
B. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward- SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on- premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.
C. Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the simba.googlebigqueryodbc.ini file to point to your data center's NAT gateway.
D. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on-premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.

Antwort: D

417. Frage

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Professional-Data-Engineer Studienmaterialien: Google Certified Professional Data Engineer Exam - Professional-Data-Engineer Torrent Prüfung & Professional-Data-Engineer wirkliche Prüfung

Google Professional-Data-Engineer Exam Syllabus Topics:

Professional-Data-Engineer neuester Studienführer & Professional-Data-Engineer Training Torrent prep

Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q412-Q417):

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