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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q396-Q401):

NEW QUESTION # 396
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

A. Error handling in the subscriber code is not handling run-time errors properly.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Total outstanding messages exceed the 10-MB maximum.
E. Publisher throughput quota is too small.

Answer: A,C

Explanation:
C, E: By not acknowleding the pulled message, this result in it be putted back in Cloud Pub/Sub, meaning the messages accumulate instead of being consumed and removed from Pub/Sub. The same thing can happen ig the subscriber maintains the lease on the message it receives in case of an error. This reduces the overall rate of processing because messages get stuck on the first subscriber. Also, errors in Cloud Function do not show up in Stackdriver Log Viewer if they are not correctly handled.
A: No problem with publisher rate as the observed result is a higher number of messages and not a lower number.
B: if messages exceed the 10MB maximum, they cannot be published.
D: Cloud Functions automatically scales so they should be able to keep up.

NEW QUESTION # 397
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

A. dataflow.developer
B. dataflow.compute
C. dataflow.worker
D. dataflow.viewer

Answer: C

Explanation:
The dataflow.worker role provides the permissions necessary for a Compute Engine service account to execute work units for a Dataflow pipeline Reference:
https://cloud.google.com/dataflow/access-control

NEW QUESTION # 398
You are updating the code for a subscriber to a Pub/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. Your subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

A. Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successfully acknowledged. If an error occurs after deployment, re-deliver any messages captured by the dead-letter queue.
B. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created.
C. Set up the Pub/Sub emulator on your local machine. Validate the behavior of your new subscriber logic before deploying it to production.
D. Use Cloud Build for your deployment. If an error occurs after deployment, use a Seek operation to locate a timestamp logged by Cloud Build at the start of the deployment.

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/pubsub/docs/replay-overview

NEW QUESTION # 399
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

A. Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas
B. Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery
C. Create a Memorystore instance with a high availability (HA) configuration
D. Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes

Answer: D

NEW QUESTION # 400
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?

A. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
B. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
C. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
D. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

Answer: A

Explanation:
Topic 2, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
Tomcat - Java services
Nginx - static content
Batch servers
Storage appliances
iSCSI for virtual machine (VM) hosts
Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
Apache Hadoop /Spark servers
Core Data Lake
Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.

NEW QUESTION # 401
......

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