Getcertkey Google GCP-DE Exam Questions in PDF Format

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| 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. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 2. Implement batch processing systems
- 3. Implement streaming data 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
|
| Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Optimize pipeline performance and cost
- 2. Design orchestration workflows
- 3. Build ETL and ELT pipelines
- 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. Tune query and pipeline performance
- 2. Optimize storage and compute usage
- 3. Control operational costs
- Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Scale data workloads efficiently
- 3. Design highly available systems
|
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Google Data Engineer Sample Questions (Q54-Q59):
NEW QUESTION # 54
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. HTTPS
- B. HTTP
- C. SOCKS
- D. VPN
Answer: C
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 55
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Add ", UNNEST(city)" before the WHERE clause.
- B. Add ", UNNEST(person)" before the WHERE clause.
- C. Change "person" to "person.city".
- D. Change "person" to "city.person".
Answer: B
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 56
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
- A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- B. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
- C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
- D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- E. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
Answer: D
NEW QUESTION # 57
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. Dialogflow Enterprise Edition
- B. Cloud Speech-to-Text API
- C. Cloud AutoML Natural Language
- D. Cloud Natural Language API
Answer: C
NEW QUESTION # 58
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. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- D. If that number falls below 4000, send an alert.
- E. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- F. If that number falls below 4000, send an alert.
- G. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
- H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- I. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- J. Consume the stream of data in Cloud Dataflow using Kafka I
- K. 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.
- L. Set a sliding time window of 1 hour every 5 minute
- M. Consume the stream of data in Cloud Dataflow using Kafka I
Answer: A
NEW QUESTION # 59
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