Google GCP-DE Exam | GCP-DE Reliable Exam Questions - Help you Pass GCP-DE: Data Engineer Exam

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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 lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design
Topic 2: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines
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: 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. ETL/ELT workflows 2. Pub/Sub streaming ingestion

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Google Data Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
B. Use Cloud Deployment Manager to automate access provision.
C. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
F. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces

Answer: D,E

NEW QUESTION # 34
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. Replicate external Hive tables to the native ones.
B. Load the ORC files into BigQuer
C. Mount the Hive tables locally.
D. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
H. Mount the Hive tables from HDFS.
I. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
J. Mount the Hive tables locally.
K. Replicate external Hive tables to the native ones.
L. Then run the Hadoop utility to copy them do HDF

Answer: I,J

NEW QUESTION # 35
When a Cloud Bigtable node fails, is lost.

A. no data
B. all data
C. the time dimension
D. the last transaction

Answer: A

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 36
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud Spanner for storag
C. Use Cloud SQL for storag
D. Add secondary indexes to support query patterns.
E. Add secondary indexes to support query patterns.
F. Use Cloud Dataflow to transform data to support query patterns.
G. Use Cloud Spanner for storag
H. Use Cloud SQL for storag

Answer: A

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 37
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 temporary table in Cloud Spanner that will act as a buffer for new data
B. Use a larger instance type for your Cloud Dataflow workers
C. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
D. 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
E. Change the zone of your Cloud Dataflow pipeline to run in us-central1
F. Increase the number of max workers
G. 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

Answer: B,D

NEW QUESTION # 38
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