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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A) Java using MapReduce
B) PigLatin using Pig
C) HiveQL using Hive
D) Python using MapReduce

2. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) VPN
B) SOCKS
C) HTTPS
D) HTTP

3. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

4. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
B) Preemptible workers cannot use persistent disk.
C) Preemptible workers cannot store data.
D) A Dataproc cluster cannot have only preemptible workers.

5. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use Cloud Dataflow to run your transformation
B) Monitor the total execution time for a sampling of job
C) Use the diagnose command to generate an operational output archiv
D) Use Cloud Dataproc to run your transformation
E) Configure the job to use non-default Compute Engine machine types when needed.
F) Resize the number of worker nodes in your cluster via the command line.
G) Monitor CPU utilization for the cluste
H) Use Cloud Dataflow to run your transformation
I) Monitor the job system lag with Stackdrive
J) Locate the bottleneck and adjust cluster resources.
K) Use Cloud Dataproc to run your transformation
L) Use the default autoscaling setting for worker instances.

Solutions: