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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q70-Q75):

NEW QUESTION # 70
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

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

Answer: B,C

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 # 71
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger based on element size in bytes
C. Trigger based on time
D. Trigger that is a combination of other triggers

Answer: B

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 72
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a SideInput that returns a Boolean if the element is corrupt.

Answer: A

NEW QUESTION # 73
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
C. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Answer: C

NEW QUESTION # 74
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
B. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
C. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
D. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
E. Set a sliding time window of 1 hour every 5 minute
F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
G. Consume the stream of data in Cloud Dataflow using Kafka I
H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. If that number falls below 4000, send an alert.
J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
K. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
L. Consume the stream of data in Cloud Dataflow using Kafka I
M. If that number falls below 4000, send an alert.

Answer: A

NEW QUESTION # 75
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