GCP-DE Vce Exam | Practice Test GCP-DE Fee

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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. Optimizing resource usage and costs 2. Automating deployment and management
Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance - Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration
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. Configuring storage for access patterns 2. Implementing cost optimization strategies
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Developing batch and streaming workflows 2. Handling data quality and consistency 3. Implementing ingestion mechanisms

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Google Data Engineer Sample Questions (Q60-Q65):

NEW QUESTION # 60
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B. Merge the transformed tables together with a SQL query
C. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Answer: F

NEW QUESTION # 61
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. Cloud Bigtable
B. Cloud Datastore
C. Cloud SQL
D. BigQuery

Answer: C

NEW QUESTION # 62
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A. SOCKS
B. HTTP
C. VPN
D. HTTPS

Answer: A

Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 63
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A. application node
B. worker node
C. conditional node
D. master node

Answer: D

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 64
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create an additional table with only the necessary columns.
C. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
D. Export the data into a Google Sheet for virtualization.

Answer: A

NEW QUESTION # 65
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