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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
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. Designing for data transformation and enrichment 2. Defining architecture for batch and streaming processing 3. Planning for data migration and integration
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs
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. Designing data warehouses and data lakes 2. Managing data lifecycle and retention 3. Using data storage services appropriately

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Google Data Engineer Sample Questions (Q70-Q75):

NEW QUESTION # 70
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. Transform
D. PCollection

Answer: C

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 # 71
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud SQL for storag
B. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud Spanner for storag
D. Use Cloud Dataflow to transform data to support query patterns.
E. Use Cloud Spanner for storag
F. Add secondary indexes to support query patterns.
G. Use Cloud SQL for storag
H. Add secondary indexes to support query patterns.

Answer: B

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 72
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B. You can configure access control only at the project level.
C. To give a user access to only one table in a project, you must configure access through your application.
D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: A

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 73
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. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
B. 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.
C. If that number falls below 4000, send an alert.
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. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
F. If that number falls below 4000, send an alert.
G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
I. Set a sliding time window of 1 hour every 5 minute
J. Consume the stream of data in Cloud Dataflow using Kafka I
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
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: A

NEW QUESTION # 74
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Instead of feeding in each feature individually, average their values in batches of 3.
B. Remove the features that have null values for more than 50% of the training records.
C. Eliminate features that are highly correlated to the output labels.
D. Combine highly co-dependent features into one representative feature.

Answer: D

NEW QUESTION # 75
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