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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Operationalizing machine learning models26%- Model deployment and monitoring
  • 1. Online vs batch prediction
    • 2. Model monitoring and drift detection
      - ML pipeline integration
      • 1. Feature engineering and feature stores
        • 2. Vertex AI pipeline deployment
          Ensuring solution quality28%- Reliability and performance
          • 1. Fault tolerance and recovery strategies
            • 2. Monitoring pipelines and workloads
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  Designing data processing systems22%- Batch and streaming data processing design
                  • 1. Latency, throughput, and consistency trade-offs
                    • 2. Event-driven vs batch architectures
                      - Data architecture and storage design
                      • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                        • 2. Designing scalable and cost-effective data models
                          Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Streaming ingestion (Pub/Sub, Dataflow)
                            • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
                              - Data processing and transformation
                              • 1. ETL/ELT pipeline design
                                • 2. Using Dataproc, Dataflow, and BigQuery SQL

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q167-Q172):

                                  NEW QUESTION # 167
                                  You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

                                  Answer: D

                                  Explanation:
                                  A dual-region Cloud Storage bucket is a type of bucket that stores data redundantly across two regions within the same continent. This provides higher availability and durability than a regional bucket, which stores data in a single region. A dual-region bucket also provides lower latency and higher throughput than a multi-regional bucket, which stores data across multiple regions within a continent or across continents. A dual-region bucket with turbo replication enabled is a premium option that offers even faster replication across regions, but it is more expensive and not necessary for this scenario.
                                  By using a dual-region Cloud Storage bucket, you can ensure that your data is protected from regional outages, and that you can access it from either region with low latency and high performance. You can also monitor the Dataflow metrics with Cloud Monitoring to determine when an outage occurs, and seek the subscription back in time by 15 minutes to recover the acknowledged messages. Seeking a subscription allows you to replay the messages from a Pub/Sub topic that were published within the message retention duration, which is one day in this case. By seeking the subscription back in time by 15 minutes, you can meet the RPO of 15 minutes, which means the maximum amount of data loss that is acceptable for your business. You can then start the Dataflow job in a secondary region and write to the same dual-region bucket, which will resume the processing of the messages and prevent data loss.
                                  Option A is not a good solution, as using a regional Cloud Storage bucket does not provide any redundancy or protection from regional outages. If the region where the bucket is located experiences an outage, you will not be able to access your data or write new data to the bucket. Seeking the subscription back in time by one day is also unnecessary and inefficient, as it will replay all the messages from the past day, even though you only need to recover the messages from the past 15 minutes.
                                  Option B is not a good solution, as using a multi-regional Cloud Storage bucket does not provide the best performance or cost-efficiency for this scenario. A multi-regional bucket stores data across multiple regions within a continent or across continents, which provides higher availability and durability than a dual-region bucket, but also higher latency and lower throughput. A multi-regional bucket is more suitable for serving data to a global audience, not for processing data with Dataflow within a single continent. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes.
                                  Option D is not a good solution, as using a dual-region Cloud Storage bucket with turbo replication enabled does not provide any additional benefit for this scenario, but only increases the cost. Turbo replication is a premium option that offers faster replication across regions, but it is not required to meet the RPO of 15 minutes. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes. Reference: Storage locations | Cloud Storage | Google Cloud, Dataflow metrics | Cloud Dataflow | Google Cloud, Seeking a subscription | Cloud Pub/Sub | Google Cloud, Recovery point objective (RPO) | Acronis.


                                  NEW QUESTION # 168
                                  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?

                                  Answer: D


                                  NEW QUESTION # 169
                                  Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

                                  Answer: D

                                  Explanation:
                                  Topic 2, 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.


                                  NEW QUESTION # 170
                                  Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable.
                                  The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data.
                                  They want to improve this performance while minimizing cost. What should they do?

                                  Answer: A


                                  NEW QUESTION # 171
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
                                  ____.

                                  Answer: D

                                  Explanation:
                                  Explanation
                                  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
                                  Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


                                  NEW QUESTION # 172
                                  ......

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