最新GCP-DE題庫資源＆資格考試的領導者和Google Data Engineer

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Optimize pipeline performance and cost 3. Build ETL and ELT pipelines - Designing for data ingestion 1. Select appropriate storage systems 2. Design scalable and reliable ingestion architecture 3. Design batch and streaming ingestion solutions
Ensuring solution quality	20%-25%	- Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage - Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls
Building and operationalizing data processing systems	28%-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. Monitor data pipelines and workloads 2. Manage logging and alerting 3. Implement fault tolerance and recovery
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Tune query and pipeline performance 2. Optimize storage and compute usage 3. Control operational costs - Reliability and scalability 1. Implement disaster recovery strategies 2. Design highly available systems 3. Scale data workloads efficiently

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最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q55-Q60):

問題 #55
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.

A. Denormalize the data as must as possible.
B. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
C. Preserve the structure of the data as much as possible.
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Use BigQuery'ssupport for external data sources to query.
F. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil

答案：B,F

問題 #56
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: date#data_pointColumn data: device_id
B. Rowkey: device_idColumn data: date, data_point
C. Rowkey: date#device_idColumn data: data_point
D. Rowkey: dateColumn data: device_id, data_point
E. Rowkey: data_pointColumn data: device_id, date

答案：E

問題 #57
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on time
B. Trigger based on element size in bytes
C. Trigger that is a combination of other triggers
D. Trigger based on element count

答案：B

解題說明：
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

問題 #58
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
B. If that number falls below 4000, send an alert.
C. If that number falls below 4000, send an alert.
D. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
E. Consume the stream of data in Cloud Dataflow using Kafka I
F. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
G. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
H. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
J. Consume the stream of data in Cloud Dataflow using Kafka I
K. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
L. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
M. Set a sliding time window of 1 hour every 5 minute

答案：G

問題 #59
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. A processing time trigger
B. The with Allowed Lateness method
C. An hourly watermark
D. An event time trigger

答案：A

解題說明：
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

問題 #60
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