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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Storing and managing data	20%	- Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Orchestrating and scheduling jobs 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Ingesting and processing data	25%	- Transforming data 1. Applying data processing logic 2. Optimizing transformations for performance 3. Managing schema evolution - Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows
Designing data processing systems	24%	- Planning data solutions 1. Planning for data security and compliance 2. Selecting appropriate storage solutions 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance

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Google Data Engineer Sample Questions (Q32-Q37):

NEW QUESTION # 32
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. In the event of an emergency, use a point-in-time snapshot to recover the data.
B. Set the BigQuery dataset to be regiona
C. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. In the event of an emergency, use the backup copy of the table.
E. Set the BigQuery dataset to be regiona
F. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
G. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
H. In the event of an emergency, use the backup copy of the table.
I. Set the BigQuery dataset to be multi-regional
J. Set the BigQuery dataset to be multi-regional

Answer: A

NEW QUESTION # 33
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can be programmed in Java
B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines can consume data from other Google Cloud services

Answer: C

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 34
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 Pub/Sub, Cloud SQL, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer: B

NEW QUESTION # 35
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A. Increase the size of the Google Persistent Disk on your server.
B. Increase the CPU size on your server.
C. Increase your network bandwidth from Compute Engine to Cloud Storage.
D. Increase your network bandwidth from your datacenter to GCP.

Answer: D

NEW QUESTION # 36
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Remove the features that have null values for more than 50% of the training records.
B. Eliminate features that are highly correlated to the output labels.
C. Combine highly co-dependent features into one representative feature.
D. Instead of feeding in each feature individually, average their values in batches of 3.

Answer: C

NEW QUESTION # 37
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