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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Orchestrating and scheduling jobs
  • 2. Ensuring reliability and recoverability
  • 3. Monitoring and troubleshooting pipelines
- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
Topic 2: 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. Handling data quality and consistency
  • 3. Developing batch and streaming workflows
Topic 3: Storing and managing data20%- Implementing storage solutions
  • 1. Using data storage services appropriately
  • 2. Managing data lifecycle and retention
  • 3. Designing data warehouses and data lakes
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Topic 4: 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. Planning for data migration and integration
  • 2. Defining architecture for batch and streaming processing
  • 3. Designing for data transformation and enrichment
Topic 5: Preparing data for analysis and machine learning13%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
- Enabling data analysis
  • 1. Implementing data sharing and governance
  • 2. Optimizing query performance
  • 3. Preparing datasets for querying and reporting

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Google Data Engineer Sample Questions (Q41-Q46):

NEW QUESTION # 41
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

Answer: A


NEW QUESTION # 42
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Answer: B


NEW QUESTION # 43
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: B,E


NEW QUESTION # 44
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

Answer: A,C

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


NEW QUESTION # 45
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?

Answer: A


NEW QUESTION # 46
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