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Questions in desktop-based mock exams are identical to the real ones. Our practice exams give you options to change their durations and questions' numbers to polish your skills. You can easily assess your readiness with the assistance of results produced by the practice exam. This Data Engineer software records all your previous takes so you can identify your mistakes and overcome them before the final attempt. The Data Engineer (GCP-DE) desktop practice exam software works only on Windows operating system.

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines
Topic 2: 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. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
Topic 3: Designing data processing systems	24%	- Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 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
Topic 4: 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. Applying data processing logic 3. Managing schema evolution
Topic 5: Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes

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Google Data Engineer Sample Questions (Q44-Q49):

NEW QUESTION # 44
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision AutoML, but reduce your dataset twice.
B. Use Cloud Vision API by providing custom labels as recognition hints.
C. Use Cloud Vision AutoML with the existing dataset.
D. Train your own image recognition model leveraging transfer learning techniques.

Answer: C

NEW QUESTION # 45
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

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

Answer: C

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 # 46
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Bigtable
B. BigQuery
C. Cloud Dataflow
D. Cloud Datastore

Answer: B

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 47
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 1 minute
B. Windows at every 10 minutes
C. Windows at every 100 MB of data
D. Single, Global Window

Answer: D

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 48
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. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
B. If that number falls below 4000, send an alert.
C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
E. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
F. If that number falls below 4000, send an alert.
G. 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.
H. Set a sliding time window of 1 hour every 5 minute
I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
L. Consume the stream of data in Cloud Dataflow using Kafka I
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: C

NEW QUESTION # 49
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