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Google Data Engineer Sample Questions:

1. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A) Introduce data compression for each file to increase the rate file of file transfer.
B) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
D) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E) Assemble 1,000 files into a tape archive (TAR) fil
F) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

2. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
B) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D) Merge the transformed tables together with a SQL query
E) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

3. 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) 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.
B) Partition the tables by a column containing a TIMESTAMP or DATE Type.
C) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
D) 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.
E) Re-create the tables using DD
F) Write an Apache Beam pipeline that creates a BigQuery table per data

4. 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) Preemptible workers cannot use persistent disk.
C) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D) Preemptible workers cannot store data.

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

Solutions: