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| Section | Weight | Objectives |
|---|
| Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
- Operationalizing workloads
- 1. Ensuring reliability and recoverability
- 2. Orchestrating and scheduling jobs
- 3. Monitoring and troubleshooting pipelines
|
| Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Supporting model training and serving
- Enabling data analysis
- 1. Optimizing query performance
- 2. Implementing data sharing and governance
- 3. Preparing datasets for querying and reporting
|
| Storing and managing data | 20% | - 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. Implementing cost optimization strategies
- 2. Configuring storage for access patterns
|
| Ingesting and processing data | 25% | - Transforming data
- 1. Managing schema evolution
- 2. Applying data processing logic
- 3. Optimizing transformations for performance
- Building data pipelines
- 1. Implementing ingestion mechanisms
- 2. Handling data quality and consistency
- 3. Developing batch and streaming workflows
|
| Designing data processing systems | 24% | - 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
- Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Selecting appropriate storage solutions
- 3. Planning for data security and compliance
|
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Google Data Engineer Sample Questions (Q32-Q37):
NEW QUESTION # 32
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?
- A. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
- B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
- C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
Answer: D
NEW QUESTION # 33
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- D. Create a cron schedule in Cloud Dataprep.
Answer: A
NEW QUESTION # 34
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?
- A. Cloud Bigtable
- B. BigQuery
- C. Cloud Datastore
- D. Cloud SQL for PostgreSQL
Answer: B
NEW QUESTION # 35
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
- A. Introduce resource hierarchy to leverage access control policy inheritance.
- B. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
- E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- F. Use Cloud Deployment Manager to automate access provision.
Answer: A,E
NEW QUESTION # 36
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
- A. only if
- B. once
- C. before
- D. after
Answer: C
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 37
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