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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems - Operationalizing systems 1. Monitor data pipelines and workloads 2. Manage logging and alerting 3. Implement fault tolerance and recovery
Topic 2: Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Implement encryption and compliance controls 3. Protect sensitive data - Data quality management 1. Validate and clean data 2. Implement data governance 3. Maintain metadata and lineage
Topic 3: Designing data processing systems	22%-27%	- Designing data pipelines 1. Optimize pipeline performance and cost 2. Build ETL and ELT pipelines 3. Design orchestration workflows - Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Select appropriate storage systems 3. Design batch and streaming ingestion solutions
Topic 4: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs - Reliability and scalability 1. Design highly available systems 2. Scale data workloads efficiently 3. Implement disaster recovery strategies

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Answer: B

NEW QUESTION # 74
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud Natural Language API
B. Dialogflow Enterprise Edition
C. Cloud Speech-to-Text API
D. Cloud AutoML Natural Language

Answer: D

NEW QUESTION # 75
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. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
B. 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
C. Use a larger instance type for your Cloud Dataflow workers
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. Change the zone of your Cloud Dataflow pipeline to run in us-central1
G. Create a temporary table in Cloud Spanner that will act as a buffer for new data

Answer: C,E

NEW QUESTION # 76
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. Use BigQuery UPDATE to further reduce the size of the dataset.
B. Use BigQuery'ssupport for external data sources to query.
C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D. Denormalize the data as must as possible.
E. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F. Preserve the structure of the data as much as possible.

Answer: C,E

NEW QUESTION # 77
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 API by providing custom labels as recognition hints.
B. Train your own image recognition model leveraging transfer learning techniques.
C. Use Cloud Vision AutoML, but reduce your dataset twice.
D. Use Cloud Vision AutoML with the existing dataset.

Answer: D

NEW QUESTION # 78
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