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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration - Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Batch processing 2. Streaming (e.g., windowing, late arriving data) 3. AI data enrichment 4. Data acquisition and import 5. Integrating with new data sources 6. Processing logic - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements - Designing data processing resources 1. Cost optimization 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cluster sizing and autoscaling
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows) - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Performing security considerations 1. Data encryption 2. Auditing, privacy, and compliance 3. Identity and Access Management (IAM)
Topic 4: Storing the data (~20% of the exam)	20%	- Using a data lake 1. Monitoring the data lake 2. Managing the lake (data discovery, access, cost controls) 3. Processing data - Planning for using a data warehouse 1. Deciding the degree of data normalization 2. Designing the data model 3. Defining architecture to support data access patterns 4. Mapping business requirements - Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems - Selecting storage systems 1. Analyzing data access patterns 2. Lifecycle management of data 3. Planning for storage costs and performance
Topic 5: Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs - Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Planning for monitoring and alerting 3. Recovering from failures

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최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q231-Q236):

질문 # 231
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account. What should you do?

A. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
C. Create an additional project to overcome the 2K on-demand per-project quota.
D. Convert your batch BQ queries into interactive BQ queries.

정답：A

질문 # 232
Case Study 2 - MJTelco
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

A. The number of workers
B. The maximum number of workers
C. The zone
D. The disk size per worker

정답：B

질문 # 233
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A.
The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

A. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.
B. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
C. Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
D. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.

정답：C

설명：
Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.

질문 # 234
You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

A. 1. Create a retries column in the sensor? table.
2. Set record type and repeated mode for the metrics column.
3. Use an UPDATE statement every 30 seconds to add new metrics.
B. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an INSERT statement every 30 seconds to add new metrics.
C. 1. Create a metrics table partitioned by timestamp.
2. Create a sensor Id column in the metrics table, that points to the _d column in the sensors table.
3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
D. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorld column in the metrics table, that points to the id column in the sensors table.
3. Use an IHSEW statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.

정답：D

설명：
For a farming company with a sensor data table updated every 30 seconds, the goal is to minimize costs while facilitating weekly analytical queries. The best data model will effectively manage data storage, update frequency, and query performance.
* Partitioned Metrics Table:
* Creating a metrics table partitioned by timestamp optimizes query performance and storage costs.
* Partitioning by timestamp allows for efficient querying, especially for time-based analyses.
* Sensor ID Reference:
* Including a sensor_id column in the metrics table that points to the id column in the sensors table ensures data normalization.
* This structure avoids redundancy and maintains a clear relationship between sensors and their metrics.
* Using INSERT Statements:
* Using INSERT statements to append new metrics every 30 seconds is efficient and cost-effective.
* INSERT operations are more suitable than UPDATE operations for adding new data entries, especially at high frequencies.
* Joining Tables for Analysis:
* When running analytical queries, joining the partitioned metrics table with the sensors table as needed provides a comprehensive view of the data.
* This approach leverages BigQuery's powerful JOIN capabilities while keeping the data model normalized and efficient.
Google Data Engineer References:
* BigQuery Partitioned Tables
* BigQuery Best Practices
* Efficient Data Partitioning
* BigQuery Data Modeling
Using this data model, the farming company can manage its sensor data effectively, minimize costs, and perform weekly analytical queries with high efficiency.

질문 # 235
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. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
B. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
C. Create groups for your users and give those groups access to the dataset
D. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

정답：D

설명：
Explanation

질문 # 236
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