Google Cloud Certified GCP-DE

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Configure your Cloud Dataflow pipeline to use local execution
B) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
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) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

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

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

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

4. Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A) Create an additional table with only the necessary columns.
B) Create a view on the table to present to the virtualization tool.
C) Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
D) Export the data into a Google Sheet for virtualization.

5. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Write an Apache Beam pipeline that creates a BigQuery table per data
B) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
C) Partition the tables by a column containing a TIMESTAMP or DATE Type.
D) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
E) Re-create the tables using DD
F) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Solutions: