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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage
Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design

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Google Data Engineer Sample Questions (Q46-Q51):

NEW QUESTION # 46
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

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

Answer: D

NEW QUESTION # 47
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Read and write to Google Cloud Storage; write to Google Cloud Logging
C. Execute to Google Cloud Storage; execute to Google Cloud Logging
D. Execute to Google Cloud Storage; write to Google Cloud Logging

Answer: B

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 48
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. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
B. 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
C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E. Merge the transformed tables together with a SQL query
F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Answer: D

NEW QUESTION # 49
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
B. Use Cloud Deployment Manager to automate access provision.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: C,D

NEW QUESTION # 50
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: dateColumn data: device_id, data_point
B. Rowkey: data_pointColumn data: device_id, date
C. Rowkey: device_idColumn data: date, data_point
D. Rowkey: date#data_pointColumn data: device_id
E. Rowkey: date#device_idColumn data: data_point

Answer: B

NEW QUESTION # 51
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