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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparing data for analysis and machine learning | 13% | - Enabling data analysis
|
| Topic 2: Ingesting and processing data | 25% | - Building data pipelines
|
| Topic 3: Storing and managing data | 20% | - Optimizing storage performance and cost
|
| Topic 4: Maintaining and automating data workloads | 18% | - Automation and optimization
|
| Topic 5: Designing data processing systems | 24% | - Designing data pipelines
|
Google Data Engineer Sample Questions:
Question #1
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
A. Spark
B. YARN
C. Hive
D. Pig
Question #2
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 BigQuer
B. Consume the stream of data in Cloud Dataflow using Kafka I
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
D. If that number falls below 4000, send an alert.
E. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
F. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
G. Set a sliding time window of 1 hour every 5 minute
H. If that number falls below 4000, send an alert.
I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K. 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.
L. Consume the stream of data in Cloud Dataflow using Kafka I
M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
Question #3
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
A. Cloud Dataflow
B. Cloud Bigtable
C. BigQuery
D. Cloud Datastore
Question #4
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
A. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
Question #5
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?
A. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
Solutions:
| Question #1 Correct Answer: A,C,D | Question #2 Correct Answer: E | Question #3 Correct Answer: C | Question #4 Correct Answer: D | Question #5 Correct Answer: D |


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