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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

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

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

4. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Cloud AutoML Natural Language
B) Cloud Natural Language API
C) Dialogflow Enterprise Edition
D) Cloud Speech-to-Text API

5. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
B) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
C) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
D) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Solutions: