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Google Data Engineer Sample Questions:

1. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A) Cloud Functions
B) Cloud Scheduler
C) Cloud Composer
D) Cloud Dataflow

2. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Convert the sharded tables into a single partitioned table
B) Enable query caching so you can cache data from previous months
C) Convert all daily log tables into date-partitioned tables
D) Create separate views to cover each month, and query from these views

3. When a Cloud Bigtable node fails, is lost.

A) all data
B) the time dimension
C) the last transaction
D) no data

4. 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) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B) Create an application that provides an AP
C) 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.
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) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

5. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Call the Cloud Natural Language API from your applicatio
B) Call the model from your application and process the results as labels.
C) Call the model from your application and process the results as labels.
D) Deploy the model using Cloud Machine Learning Engin
E) Deploy the model using a KubernetesEngine cluste
F) Call the Cloud Natural Language API from your applicatio
G) Process the generated Sentiment Analysis as labels.
H) Build and train a text classification model using TensorFlo
I) Process the generated Entity Analysis as labels.
J) Build and train a text classification model using TensorFlo

Solutions: