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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 2: Designing data processing systems	20%	- Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection
Topic 3: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing
Topic 4: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage

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Google Data Engineer Sample Questions (Q52-Q57):

NEW QUESTION # 52
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Dataflow
B. Cloud Functions
C. Cloud Composer
D. Cloud Scheduler

Answer: D

NEW QUESTION # 53
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer: C

NEW QUESTION # 54
When a Cloud Bigtable node fails, is lost.

A. no data
B. all data
C. the last transaction
D. the time dimension

Answer: A

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 55
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 a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
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. If that number falls below 4000, send an alert.
F. Set a sliding time window of 1 hour every 5 minute
G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
J. If that number falls below 4000, send an alert.
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. 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.
M. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl

Answer: B

NEW QUESTION # 56
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Dataflow
B. BigQuery
C. Cloud Bigtable
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 # 57
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