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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Protect sensitive data 3. Manage IAM and access control - Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage
Topic 2: Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Build ETL and ELT pipelines 3. Design orchestration workflows
Topic 3: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement streaming data systems 2. Implement batch processing systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services - Operationalizing systems 1. Implement fault tolerance and recovery 2. Monitor data pipelines and workloads 3. Manage logging and alerting
Topic 4: Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Scale data workloads efficiently 2. Design highly available systems 3. Implement disaster recovery strategies - Managing resources and costs 1. Control operational costs 2. Tune query and pipeline performance 3. Optimize storage and compute usage

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Google Data Engineer Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element size in bytes
B. Trigger based on element count
C. Trigger based on time
D. Trigger that is a combination of other triggers

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 22
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. Enable query caching so you can cache data from previous months
B. Convert the sharded tables into a single partitioned table
C. Convert all daily log tables into date-partitioned tables
D. Create separate views to cover each month, and query from these views

Answer: C

NEW QUESTION # 23
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
C. Increase the number of max workers
D. Change the zone of your Cloud Dataflow pipeline to run in us-central1
E. Create a temporary table in Cloud Spanner that will act as a buffer for new data
F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
G. Use a larger instance type for your Cloud Dataflow workers

Answer: F,G

NEW QUESTION # 24
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. after
B. only if
C. once
D. before

Answer: D

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 25
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. Change "person" to "person.city".
B. Add ", UNNEST(city)" before the WHERE clause.
C. Add ", UNNEST(person)" before the WHERE clause.
D. Change "person" to "city.person".

Answer: C

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION # 26
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