Exam Professional-Data-Engineer Topics, Professional-Data-Engineer Visual Cert Test

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

>> Exam Professional-Data-Engineer Topics <<

Google Professional-Data-Engineer Visual Cert Test | VCE Professional-Data-Engineer Dumps

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Google Certified Professional Data Engineer Exam Sample Questions (Q279-Q284):

NEW QUESTION # 279
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
B. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
D. Put the data into Google Cloud Storage.

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/dataproc/

NEW QUESTION # 280
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. 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
B. Create groups for your users and give those groups access to the dataset
C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
D. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Answer: D

NEW QUESTION # 281
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

A. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
B. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level
C. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
D. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Answer: B

NEW QUESTION # 282
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. Increase the share of the test sample in the train-test split.
B. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
C. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
D. Try to collect more data and increase the size of your dataset.

Answer: C

NEW QUESTION # 283
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub, processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

A. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore.
B. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
C. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
D. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore.

Answer: D

NEW QUESTION # 284
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