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Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

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

2. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

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

3. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

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

4. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
B) Deploy a Cloud Dataproc cluste
C) Install the Cloud Storage connector, and store the data in Cloud Storag
D) Use an SSD persistent disk and 50% preemptible worker
E) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F) Change references in scripts from hdfs:// to gs://
G) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
H) Deploy a Cloud Dataproc cluste
I) Use a standard persistent disk and 50% preemptible worker
J) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
K) Change references in scripts from hdfs:// to gs://