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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Applying data processing logic 2. Optimizing transformations for performance 3. Managing schema evolution - Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows
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. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Monitoring and troubleshooting pipelines 3. Ensuring reliability and recoverability - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
Designing data processing systems	24%	- 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 - Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions

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Google Data Engineer Sample Questions (Q42-Q47):

NEW QUESTION # 42
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
B. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Answer: D

NEW QUESTION # 43
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 hour that counts the number of rows created in Cloud Bigtable in the last hour
B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
C. If that number falls below 4000, send an alert.
D. Consume the stream of data in Cloud Dataflow using Kafka I
E. 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.
F. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
G. Set a sliding time window of 1 hour every 5 minute
H. If that number falls below 4000, send an alert.
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su

Answer: L

NEW QUESTION # 44
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Spark
B. Hive
C. Pig
D. YARN

Answer: A,B,C

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run

NEW QUESTION # 45
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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Use K-means Clustering to detect faces in the pixels.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: A

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 # 46
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 Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

NEW QUESTION # 47
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