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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
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. BigQuery ML usage 2. Vertex AI integration for pipelines
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
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

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Google Data Engineer Sample Questions (Q63-Q68):

NEW QUESTION # 63
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Use a larger instance type for your Cloud Dataflow workers
B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
C. Create a temporary table in Cloud Spanner that will act as a buffer for new data
D. Increase the number of max workers
E. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
F. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G. Change the zone of your Cloud Dataflow pipeline to run in us-central1

Answer: A,E

NEW QUESTION # 64
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. PigLatin using Pig
B. Java using MapReduce
C. HiveQL using Hive
D. Python using MapReduce

Answer: D

NEW QUESTION # 65
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. 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.
B. Set a sliding time window of 1 hour every 5 minute
C. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
E. If that number falls below 4000, send an alert.
F. If that number falls below 4000, send an alert.
G. Consume the stream of data in Cloud Dataflow using Kafka I
H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. Consume the stream of data in Cloud Dataflow using Kafka I
J. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
L. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour

Answer: L

NEW QUESTION # 66
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A. Cloud SQL for PostgreSQL
B. Cloud Datastore
C. Cloud Bigtable
D. BigQuery

Answer: D

NEW QUESTION # 67
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A. Sink API
B. Pipeline
C. PCollection
D. Transform

Answer: D

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model

NEW QUESTION # 68
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