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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Optimize pipeline performance and cost 3. Build ETL and ELT pipelines - Designing for data ingestion 1. Select appropriate storage systems 2. Design batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement streaming data systems 3. Implement batch processing systems - Operationalizing systems 1. Monitor data pipelines and workloads 2. Implement fault tolerance and recovery 3. Manage logging and alerting
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs - Reliability and scalability 1. Design highly available systems 2. Scale data workloads efficiently 3. Implement disaster recovery strategies
Ensuring solution quality	20%-25%	- Data quality management 1. Validate and clean data 2. Implement data governance 3. Maintain metadata and lineage - Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls

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Google Data Engineer Sample Questions (Q50-Q55):

NEW QUESTION # 50
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Answer: C

NEW QUESTION # 51
Which of these is not a supported method of putting data into a partitioned table?

A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
B. Create a partitioned table and stream new records to it every day.
C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

Answer: C

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 52
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 K-means Clustering to detect faces in the pixels.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: C

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 # 53
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. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
C. Consume the stream of data in Cloud Dataflow using Kafka I
D. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
E. Consume the stream of data in Cloud Dataflow using Kafka I
F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
G. Set a sliding time window of 1 hour every 5 minute
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
J. If that number falls below 4000, send an alert.
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. If that number falls below 4000, send an alert.
M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer

Answer: B

NEW QUESTION # 54
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: data_pointColumn data: device_id, date
B. Rowkey: dateColumn data: device_id, data_point
C. Rowkey: device_idColumn data: date, data_point
D. Rowkey: date#data_pointColumn data: device_id
E. Rowkey: date#device_idColumn data: data_point

Answer: A

NEW QUESTION # 55
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