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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
Topic 2: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Topic 3: 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. Monitoring and troubleshooting pipelines 3. Ensuring reliability and recoverability
Topic 4: Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Designing data warehouses and data lakes 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Topic 5: Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Planning for data migration and integration 3. Designing for data transformation and enrichment

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Google Data Engineer Sample Questions (Q25-Q30):

NEW QUESTION # 25
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Sessions
B. OutputCriteria
C. Windows
D. Triggers

Answer: D

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION # 26
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. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Answer: D

NEW QUESTION # 27
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 Kafka Connect to link your Kafka message queue to Cloud Pub/Su
B. Consume the stream of data in Cloud Dataflow using Kafka I
C. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
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. If that number falls below 4000, send an alert.
F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
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. Consume the stream of data in Cloud Dataflow using Kafka I
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 sliding time window of 1 hour every 5 minute
L. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer

Answer: L

NEW QUESTION # 28
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the TABLE_DATE_RANGE function
B. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use the WHERE_PARTITIONTIME pseudo column

Answer: A

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap

NEW QUESTION # 29
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C. Create an application that writes to a Cloud SQL database to store the dat
D. Create an application that provides an AP
E. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: D

NEW QUESTION # 30
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