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Google Data Engineer Sample Questions (Q16-Q21):
NEW QUESTION # 16
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. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
- B. Configure your Cloud Dataflow pipeline to use local execution
- C. Increase the number of nodes in the Cloud Bigtable cluster
- D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
- E. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
Answer: A,E
NEW QUESTION # 17
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?
- A. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
- B. Create an application that provides an AP
- C. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
- D. Create an application that writes to a Cloud SQL database to store the dat
- E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
- F. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
Answer: B
NEW QUESTION # 18
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 identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
- B. Create a view on the table to present to the virtualization tool.
- C. Export the data into a Google Sheet for virtualization.
- D. Create an additional table with only the necessary columns.
Answer: B
NEW QUESTION # 19
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. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- B. Use BigQuery UPDATE to further reduce the size of the dataset.
- C. Preserve the structure of the data as much as possible.
- D. Denormalize the data as must as possible.
- E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
- F. Use BigQuery'ssupport for external data sources to query.
Answer: A,E
NEW QUESTION # 20
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. SOCKS
- B. HTTPS
- C. VPN
- D. HTTP
Answer: A
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 21
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