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Google Data Engineer Sample Questions:
1. 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) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B) 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
C) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
D) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E) Merge the transformed tables together with a SQL query
F) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
2. 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 the diagnose command to generate an operational output archiv
B) Monitor CPU utilization for the cluste
C) Use Cloud Dataflow to run your transformation
D) Use Cloud Dataproc to run your transformation
E) Use Cloud Dataflow to run your transformation
F) Monitor the total execution time for a sampling of job
G) Configure the job to use non-default Compute Engine machine types when needed.
H) Monitor the job system lag with Stackdrive
I) Use Cloud Dataproc to run your transformation
J) Locate the bottleneck and adjust cluster resources.
K) Resize the number of worker nodes in your cluster via the command line.
L) Use the default autoscaling setting for worker instances.
3. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A) Use feature engineering to add features for eyes, noses, and mouths to the input data.
B) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
C) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D) Use K-means Clustering to detect faces in the pixels.
4. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
A) Cloud Pub/Sub, Cloud Dataflow, and Local SSD
B) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
D) Cloud Pub/Sub, Cloud SQL, and Cloud Storage
5. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
A) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
B) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |
