Valid GCP-DE Practice Questions & GCP-DE Test Collection

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows - Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic
Topic 3: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Topic 4: 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. Using data storage services appropriately 3. Managing data lifecycle and retention
Topic 5: Designing data processing systems	24%	- 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 - Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency

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Google Data Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: B

NEW QUESTION # 27
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. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B. 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.
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 # 28
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision AutoML with the existing dataset.
B. Use Cloud Vision API by providing custom labels as recognition hints.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: A

NEW QUESTION # 29
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A. Cloud BigTable
B. BigQuery
C. Cloud SQL
D. Cloud Datastore

Answer: A

NEW QUESTION # 30
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Process the generated Entity Analysis as labels.
B. Deploy the model using a KubernetesEngine cluste
C. Call the Cloud Natural Language API from your applicatio
D. Build and train a text classification model using TensorFlo
E. Call the Cloud Natural Language API from your applicatio
F. Process the generated Sentiment Analysis as labels.
G. Call the model from your application and process the results as labels.
H. Deploy the model using Cloud Machine Learning Engin
I. Build and train a text classification model using TensorFlo
J. Call the model from your application and process the results as labels.

Answer: A

NEW QUESTION # 31
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