Google GCP-DE Study Demo & Dumps GCP-DE Collection

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 Bigtable to BigQuery
B. 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
C. Change the zone of your Cloud Dataflow pipeline to run in us-central1
D. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
E. Use a larger instance type for your Cloud Dataflow workers
F. Create a temporary table in Cloud Spanner that will act as a buffer for new data
G. Increase the number of max workers

Answer: A,E

NEW QUESTION # 52
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. HiveQL using Hive
B. PigLatin using Pig
C. Java using MapReduce
D. Python using MapReduce

Answer: D

NEW QUESTION # 53
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A. Increase the CPU size on your server.
B. Increase the size of the Google Persistent Disk on your server.
C. Increase your network bandwidth from Compute Engine to Cloud Storage.
D. Increase your network bandwidth from your datacenter to GCP.

Answer: D

NEW QUESTION # 54
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines can be programmed in Java
D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Answer: A

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 55
Which of these sources can you not load data into BigQuery from?

A. Google Cloud SQL
B. Google Cloud Storage
C. Google Drive
D. File upload

Answer: A

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION # 56
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