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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A stock symbol followed by a timestamp
B) A non-sequential numeric ID
C) A sequential numeric ID
D) A timestamp followed by a stock symbol

2. 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 provides an AP
B) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C) 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.
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 a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
F) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

3. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) An hourly watermark
B) An event time trigger
C) A processing time trigger
D) The with Allowed Lateness method

4. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
C) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D) Use BigQuery to export the data for the table to a CSV fil
E) Add a new column called FullName to the Users tabl
F) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

5. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) application node
B) master node
C) worker node
D) conditional node

Solutions: