GCP-DE Study Materials - GCP-DE Test Dumps.zip

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Google GCP-DE Exam Syllabus Topics:

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

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GCP-DE Test Dumps.zip - Latest GCP-DE Exam Test

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Google Data Engineer Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

Answer: B

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive


NEW QUESTION # 28
Which of these is not a supported method of putting data into a partitioned table?

Answer: A

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 # 29
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?

Answer: B


NEW QUESTION # 30
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?

Answer: B


NEW QUESTION # 31
Which of the following statements is NOT true regarding Bigtable access roles?

Answer: A

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control


NEW QUESTION # 32
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