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To become a Google Certified Professional Data Engineer, candidates must have a strong foundation in data engineering concepts and technologies. They must also possess excellent problem-solving skills and have a deep understanding of data analysis and interpretation. Candidates can prepare for the exam by taking online courses, attending training programs, and practicing using real-world data scenarios.

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Google Certified Professional Data Engineer Exam Sample Questions (Q376-Q381):

NEW QUESTION # 376
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2 Create a separate DAG for each table that needs to go through the pipeline
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
2 Create a separate DAG for each table that needs to go through the pipeline
3 Schedule the DAGs to run hourly
C. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline
3 Schedule the DAG to run hourly
D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline.
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: A

Explanation:
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performance of these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for the corresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. Reference:
1: Dataproc Operator | Cloud Composer | Google Cloud
2: BigQuery Operator | Cloud Composer | Google Cloud
3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
[5]: Triggering DAGs | Cloud Composer | Google Cloud
[6]: Cloud Storage Operator | Cloud Composer | Google Cloud

NEW QUESTION # 377
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'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?

A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
B. Cloud Dataflow, Cloud SQL, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
E. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer: E

Explanation:
Explanation/Reference:

NEW QUESTION # 378
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

A. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
B. Try to collect more data and increase the size of your dataset.
C. Increase the share of the test sample in the train-test split.
D. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Answer: D

NEW QUESTION # 379
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Provide latitude and longtitude as input vectors to your neural net.
B. Create a numeric column from a feature cross of latitude and longtitude.
C. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
D. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Answer: D

Explanation:
Reference https://cloud.google.com/bigquery/docs/gis-dataa

NEW QUESTION # 380
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A. Specify the TableReference object in the code.
B. Use .fromQuery operation to read specific fields from the table.
C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
D. Call a transform that returns TableRow objects, where each element in the PCollection represents a single row in the table.

Answer: D

NEW QUESTION # 381
......

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Practice Professional-Data-Engineer Test | New Professional-Data-Engineer Test Pattern

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Google Certified Professional Data Engineer Exam Sample Questions (Q376-Q381):

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