Google Data Engineer 認定 GCP-DE 試験問題:

1. What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A) Windows
B) OutputCriteria
C) Triggers
D) Sessions

2. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
B) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
C) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D) Use a standard persistent disk and 50% preemptible worker
E) Change references in scripts from hdfs:// to gs://
F) Deploy a Cloud Dataproc cluste
G) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
H) Deploy a Cloud Dataproc cluste
I) Use an SSD persistent disk and 50% preemptible worker
J) Change references in scripts from hdfs:// to gs://
K) Install the Cloud Storage connector, and store the data in Cloud Storag

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

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

4. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
B) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
C) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
D) Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

5. 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) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
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) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

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