GCP-DE Prüfungsinformationen - GCP-DE Deutsch

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Storing and managing data	20%	- Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Developing batch and streaming workflows 3. Handling data quality and consistency - Transforming data 1. Applying data processing logic 2. Optimizing transformations for performance 3. Managing schema evolution
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
Designing data processing systems	24%	- 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. Designing for data transformation and enrichment 2. Planning for data migration and integration 3. Defining architecture for batch and streaming processing
Maintaining and automating data workloads	18%	- 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

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q52-Q57):

52. Frage
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 10 minutes
B. Windows at every 100 MB of data
C. Single, Global Window
D. Windows at every 1 minute

Antwort: C

Begründung:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

53. Frage
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in Google Cloud Datastor
C. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
D. Store the data in a file in a regional Google Cloud Storage bucke
E. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
F. Load the data every 30 minutes into a new partitioned table in BigQuery.

Antwort: F

54. Frage
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
B. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
C. Increase the number of nodes in the Cloud Bigtable cluster
D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
E. Configure your Cloud Dataflow pipeline to use local execution

Antwort: A,D

55. Frage
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Split table into multiple tables; 2) Use a partitioned table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Use nested repeated fields; 2) Use a partitioned table

Antwort: C

Begründung:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

56. Frage
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. BigQuery
B. Cloud Datastore
C. Cloud Bigtable
D. Cloud Dataflow

Antwort: A

Begründung:
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

57. Frage
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