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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Topic 2: Designing data processing systems	22%	- 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. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
Topic 3: Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction - ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores
Topic 4: Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP

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Google Certified Professional Data Engineer Exam Sample Questions (Q277-Q282):

NEW QUESTION # 277
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 3090 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Modify your pipeline to maintain the last 3090 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
B. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
C. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
D. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

Answer: D

NEW QUESTION # 278
What is the HBase Shell for Cloud Bigtable?

A. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
B. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
C. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
D. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

Answer: C

Explanation:
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.

NEW QUESTION # 279
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

A. Assign global unique identifiers (GUID) to each data entry.
B. Store each data entry as the primary key in a separate database and apply an index.
C. Maintain a database table to store the hash value and other metadata for each data entry.
D. Compute the hash value of each data entry, and compare it with all historical data.

Answer: C

Explanation:
Using Hash values we can remove duplicate values from a database. Hashvalues will be same for duplicate data and thus can be easily rejected.

NEW QUESTION # 280
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
* 10 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.
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

A. Use the NOW () function in BigQuery to record the event's time.
B. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
C. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Answer: C

NEW QUESTION # 281
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

A. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
B. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
C. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
D. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

Answer: B

Explanation:
Explanation

NEW QUESTION # 282
......

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