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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Monitoring and troubleshooting pipelines
  • 2. Ensuring reliability and recoverability
  • 3. Orchestrating and scheduling jobs
- Automation and optimization
  • 1. Automating deployment and management
  • 2. Optimizing resource usage and costs
Storing and managing data20%- Implementing storage solutions
  • 1. Designing data warehouses and data lakes
  • 2. Managing data lifecycle and retention
  • 3. Using data storage services appropriately
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Preparing datasets for querying and reporting
  • 2. Optimizing query performance
  • 3. Implementing data sharing and governance
- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation
Ingesting and processing data25%- Transforming data
  • 1. Managing schema evolution
  • 2. Optimizing transformations for performance
  • 3. Applying data processing logic
- Building data pipelines
  • 1. Implementing ingestion mechanisms
  • 2. Developing batch and streaming workflows
  • 3. Handling data quality and consistency
Designing data processing systems24%- Designing data pipelines
  • 1. Designing for data transformation and enrichment
  • 2. Defining architecture for batch and streaming processing
  • 3. Planning for data migration and integration
- Planning data solutions
  • 1. Selecting appropriate storage solutions
  • 2. Planning for data security and compliance
  • 3. Designing for reliability, scalability, and efficiency

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Google Data Engineer Sample Questions (Q20-Q25):

NEW QUESTION # 20
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

Answer: B


NEW QUESTION # 21
Which of these is not a supported method of putting data into a partitioned table?

Answer: B

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


NEW QUESTION # 22
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

Answer: A

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do


NEW QUESTION # 23
What are the minimum permissions needed for a service account used with Google Dataproc?

Answer: A

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 # 24
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

Answer: A,B


NEW QUESTION # 25
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