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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
Topic 2: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 4: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging

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Google Data Engineer Sample Questions (Q30-Q35):

NEW QUESTION # 30
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 Spanner that will act as a buffer for new data
C. Change the zone of your Cloud Dataflow pipeline to run in us-central1
D. Increase the number of max workers
E. 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
F. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
G. Use a larger instance type for your Cloud Dataflow workers

Answer: E,G

NEW QUESTION # 31
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. Set the BigQuery dataset to be multi-regional
B. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be multi-regional
D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
E. Set the BigQuery dataset to be regiona
F. In the event of an emergency, use a point-in-time snapshot to recover the data.
G. In the event of an emergency, use the backup copy of the table.
H. In the event of an emergency, use a point-in-time snapshot to recover the data.
I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
J. Set the BigQuery dataset to be regiona

Answer: H

NEW QUESTION # 32
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. PigLatin using Pig
C. Python using MapReduce
D. HiveQL using Hive

Answer: C

NEW QUESTION # 33
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer: B

NEW QUESTION # 34
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. Deploy a Cloud Dataproc cluste
B. Change references in scripts from hdfs:// to gs://
C. Use a standard persistent disk and 50% preemptible worker
D. Change references in scripts from hdfs:// to gs://
E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F. Deploy a Cloud Dataproc cluste
G. Use an SSD persistent disk and 50% preemptible worker
H. Install the Cloud Storage connector, and store the data in Cloud Storag
I. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
J. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
K. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF

Answer: F

NEW QUESTION # 35
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