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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality20%-25%- Security and compliance
  • 1. Implement encryption and compliance controls
  • 2. Manage IAM and access control
  • 3. Protect sensitive data
- Data quality management
  • 1. Implement data governance
  • 2. Maintain metadata and lineage
  • 3. Validate and clean data
Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Implement disaster recovery strategies
  • 3. Design highly available systems
- Managing resources and costs
  • 1. Control operational costs
  • 2. Optimize storage and compute usage
  • 3. Tune query and pipeline performance
Designing data processing systems22%-27%- Designing for data ingestion
  • 1. Design batch and streaming ingestion solutions
  • 2. Select appropriate storage systems
  • 3. Design scalable and reliable ingestion architecture
- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Design orchestration workflows
  • 3. Optimize pipeline performance and cost
Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Implement streaming data systems
  • 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 3. Implement batch processing systems
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Implement fault tolerance and recovery
  • 3. Monitor data pipelines and workloads

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Google Data Engineer Sample Questions (Q29-Q34):

NEW QUESTION # 29
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?

Answer: A


NEW QUESTION # 30
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

Answer: B,J


NEW QUESTION # 31
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

Answer: C


NEW QUESTION # 32
When a Cloud Bigtable node fails, is lost.

Answer: C

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 33
What are all of the BigQuery operations that Google charges for?

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing


NEW QUESTION # 34
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