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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A) Create a service account and grant dataset access to that accoun
B) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
C) Create groups for your users and give those groups access to the dataset
D) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
E) Use the service account's private key to access the dataset
F) Create a dummy user and grant dataset access to that use

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 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 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.
C) Create an application that provides an AP
D) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
F) Create an application that writes to a Cloud SQL database to store the dat

3. You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A) HiveQL using Hive
B) Python using MapReduce
C) Java using MapReduce
D) PigLatin using Pig

4. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A) BigQuery
B) Cloud Bigtable
C) Cloud SQL
D) Cloud Datastore

5. 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) Preserve the structure of the data as much as possible.
B) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
C) Denormalize the data as must as possible.
D) Use BigQuery'ssupport for external data sources to query.
E) Use BigQuery UPDATE to further reduce the size of the dataset.
F) Develop a data pipeline where status updates are appended to BigQuery instead of updated.

Solutions: