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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions - 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
Topic 3: 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. Feature engineering and data preparation 2. Supporting model training and serving
Topic 4: Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately
Topic 5: Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution - Building data pipelines 1. Developing batch and streaming workflows 2. Implementing ingestion mechanisms 3. Handling data quality and consistency

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Google Data Engineer Sample Questions (Q58-Q63):

NEW QUESTION # 58
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: B

NEW QUESTION # 59
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

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

Answer: A

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

Answer: C

NEW QUESTION # 61
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Workflow Templates on Cloud Dataproc
C. Cloud Composer
D. cron

Answer: B

NEW QUESTION # 62
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Answer: B

NEW QUESTION # 63
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