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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
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. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms - Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving

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Google Data Engineer Sample Questions (Q62-Q67):

NEW QUESTION # 62
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformation
B. Use Cloud Dataflow to run your transformation
C. Use Cloud Dataproc to run your transformation
D. Monitor CPU utilization for the cluste
E. Use the diagnose command to generate an operational output archiv
F. Monitor the total execution time for a sampling of job
G. Use the default autoscaling setting for worker instances.
H. Use Cloud Dataflow to run your transformation
I. Configure the job to use non-default Compute Engine machine types when needed.
J. Resize the number of worker nodes in your cluster via the command line.
K. Locate the bottleneck and adjust cluster resources.
L. Monitor the job system lag with Stackdrive

Answer: D

NEW QUESTION # 63
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Denormalize the data as must as possible.
B. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
C. Preserve the structure of the data as much as possible.
D. Use BigQuery'ssupport for external data sources to query.
E. Use BigQuery UPDATE to further reduce the size of the dataset.
F. Develop a data pipeline where status updates are appended to BigQuery instead of updated.

Answer: B,F

NEW QUESTION # 64
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. Build and train a text classification model using TensorFlo
B. Process the generated Entity Analysis as labels.
C. Call the model from your application and process the results as labels.
D. Deploy the model using Cloud Machine Learning Engin
E. Deploy the model using a KubernetesEngine cluste
F. Call the model from your application and process the results as labels.
G. Process the generated Sentiment Analysis as labels.
H. Build and train a text classification model using TensorFlo
I. Call the Cloud Natural Language API from your applicatio
J. Call the Cloud Natural Language API from your applicatio

Answer: B

NEW QUESTION # 65
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. cron
B. Workflow Templates on Cloud Dataproc
C. Cloud Composer
D. Cloud Scheduler

Answer: B

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

Answer: C

NEW QUESTION # 67
......

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