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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Selecting appropriate storage solutions 3. Planning for data security and compliance - Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment
Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Managing data lifecycle and retention 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies
Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic - Building data pipelines 1. Developing batch and streaming workflows 2. Implementing ingestion mechanisms 3. Handling data quality and consistency
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation

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Google Data Engineer Sample Questions (Q20-Q25):

NEW QUESTION # 20
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Mount the Hive tables locally.
B. Mount the Hive tables from HDFS.
C. Load the ORC files into BigQuer
D. Then run the Hadoop utility to copy them do HDF
E. Mount the Hive tables locally.
F. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
G. Replicate external Hive tables to the native ones.
H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
I. Replicate external Hive tables to the native ones.
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
K. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
L. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table

Answer: E,J

NEW QUESTION # 21
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. A Dataproc cluster cannot have only preemptible workers.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. Preemptible workers cannot use persistent disk.

Answer: A,B

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 22
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. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
C. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP

Answer: D

NEW QUESTION # 23
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. In the event of an emergency, use the backup copy of the table.
B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
C. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
D. In the event of an emergency, use the backup copy of the table.
E. In the event of an emergency, use a point-in-time snapshot to recover the data.
F. Set the BigQuery dataset to be multi-regional
G. Set the BigQuery dataset to be multi-regional
H. Set the BigQuery dataset to be regiona
I. In the event of an emergency, use a point-in-time snapshot to recover the data.
J. Set the BigQuery dataset to be regiona

Answer: I

NEW QUESTION # 24
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. Java using MapReduce
B. HiveQL using Hive
C. PigLatin using Pig
D. Python using MapReduce

Answer: D

NEW QUESTION # 25
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