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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q42-Q47):

NEW QUESTION # 42
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. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
C. Replicate external Hive tables to the native ones.
D. Load the ORC files into BigQuer
E. Then run the Hadoop utility to copy them do HDF
F. Mount the Hive tables from HDFS.
G. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
H. Replicate external Hive tables to the native ones.
I. Mount the Hive tables locally.
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
K. Mount the Hive tables locally.
L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste

Answer: J,K

NEW QUESTION # 43
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use nested repeated fields; 2) Use a partitioned table
B. 1) Split table into multiple tables; 2) Use a partitioned table
C. 1) Use a partitioned table; 2) Join tables into one table
D. 1) Join tables into one table; 2) Use nested repeated fields

Answer: D

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 44
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: B

NEW QUESTION # 45
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 size of the Google Persistent Disk on your server.
B. Increase the CPU size on your server.
C. Increase your network bandwidth from your datacenter to GCP.
D. Increase your network bandwidth from Compute Engine to Cloud Storage.

Answer: C

NEW QUESTION # 46
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
B. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: A

NEW QUESTION # 47
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