GCP-DE Exam Simulation: Data Engineer & GCP-DE Study Guide Materials

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which of the following is NOT true about Dataflow pipelines?

Answer: D

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


NEW QUESTION # 56
Which of these is not a supported method of putting data into a partitioned table?

Answer: D

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 # 57
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Answer: C


NEW QUESTION # 58
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

Answer: B

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri


NEW QUESTION # 59
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

Answer: A


NEW QUESTION # 60
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