GCP-DE Passleader Review & GCP-DE Certification Materials

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Monitor data pipelines and workloads 2. Implement fault tolerance and recovery 3. Manage logging and alerting - Building data processing systems 1. Implement batch processing systems 2. Implement streaming data systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
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. Select appropriate storage systems 2. Design scalable and reliable ingestion architecture 3. Design batch and streaming ingestion solutions
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Control operational costs 2. Tune query and pipeline performance 3. Optimize storage and compute usage - Reliability and scalability 1. Scale data workloads efficiently 2. Implement disaster recovery strategies 3. Design highly available systems
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

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which is not a valid reason for poor Cloud Bigtable performance?

A. There are issues with the network connection.
B. The Cloud Bigtable cluster has too many nodes.
C. The workload isn't appropriate for Cloud Bigtable.
D. The table's schema is not designed correctly.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 74
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer: C

NEW QUESTION # 75
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
B. Use BigQuery to export the data for the table to a CSV fil
C. Add a new column called FullName to the Users tabl
D. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
E. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
F. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Answer: A

NEW QUESTION # 76
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the WHERE_PARTITIONTIME pseudo column
B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D. Use the TABLE_DATE_RANGE function

Answer: D

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap

NEW QUESTION # 77
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. 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.
B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C. Create an application that provides an AP
D. Create an application that writes to a Cloud SQL database to store the dat
E. 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.
F. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Answer: C

NEW QUESTION # 78
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