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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. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance
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. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows
Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance - Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration
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

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Google Data Engineer Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. If that number falls below 4000, send an alert.
B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
C. If that number falls below 4000, send an alert.
D. Set a sliding time window of 1 hour every 5 minute
E. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
F. Consume the stream of data in Cloud Dataflow using Kafka I
G. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
H. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
J. 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.
K. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
L. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: H

NEW QUESTION # 45
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Export the data into a Google Sheet for virtualization.
B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C. Create an additional table with only the necessary columns.
D. Create a view on the table to present to the virtualization tool.

Answer: D

NEW QUESTION # 46
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. Java using MapReduce
B. Python using MapReduce
C. PigLatin using Pig
D. HiveQL using Hive

Answer: B

NEW QUESTION # 47
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use feature engineering to add features for eyes, noses, and mouths to the input data.
B. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
C. Use K-means Clustering to detect faces in the pixels.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 48
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Execute to Google Cloud Storage; write to Google Cloud Logging
B. Execute to Google Cloud Storage; execute to Google Cloud Logging
C. Write to Google Cloud Storage; read to Google Cloud Logging
D. Read and write to Google Cloud Storage; write to Google Cloud Logging

Answer: D

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 49
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