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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Create separate views to cover each month, and query from these views
C) Convert all daily log tables into date-partitioned tables
D) Enable query caching so you can cache data from previous months

2. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud Spanner for storag
B) Add secondary indexes to support query patterns.
C) Use Cloud Dataflow to transform data to support query patterns.
D) Use Cloud SQL for storag
E) Add secondary indexes to support query patterns.
F) Use Cloud Spanner for storag
G) Use Cloud SQL for storag
H) Use Cloud Dataflow to transform data to support query patterns.

3. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Add ", UNNEST(city)" before the WHERE clause.
B) Add ", UNNEST(person)" before the WHERE clause.
C) Change "person" to "person.city".
D) Change "person" to "city.person".

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

5. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) worker node
B) conditional node
C) master node
D) application node

Solutions: