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| Section | Weight | Objectives |
|---|
| Topic 1: Maintaining and optimizing data and ML solutions | 20% | - Security and governance
- 1. Data encryption and compliance
- 2. IAM and access control
- Machine learning integration
- 1. BigQuery ML usage
- 2. Vertex AI integration for pipelines
|
| Topic 2: Designing data processing systems | 20% | - Data pipeline architecture design
- 1. Batch vs streaming data processing selection
- 2. Scalable data ingestion design
- Storage and data modeling
- 1. Data lake architecture on Google Cloud Storage
- 2. Data warehouse design using BigQuery
|
| Topic 3: Operationalizing data and ML pipelines | 30% | - Monitoring and troubleshooting
- 1. Logging and observability
- 2. Performance optimization and debugging
- Pipeline automation and orchestration
- 1. Cloud Composer workflows
- 2. Scheduling and monitoring pipelines
|
| Topic 4: Building and operationalizing data processing systems | 30% | - Data pipeline implementation
- 1. Dataproc and Spark-based processing
- 2. Dataflow pipeline development
- Data ingestion and transformation
- 1. Pub/Sub streaming ingestion
- 2. ETL/ELT workflows
|
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Google Data Engineer Sample Questions (Q50-Q55):
NEW QUESTION # 50
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?
- A. Set the BigQuery dataset to be multi-regional
- B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
- C. In the event of an emergency, use the backup copy of the table.
- D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
- E. In the event of an emergency, use a point-in-time snapshot to recover the data.
- F. Set the BigQuery dataset to be regiona
- G. In the event of an emergency, use a point-in-time snapshot to recover the data.
- H. Set the BigQuery dataset to be multi-regional
- I. Set the BigQuery dataset to be regiona
- J. In the event of an emergency, use the backup copy of the table.
Answer: G
NEW QUESTION # 51
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?
- A. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
- B. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
- C. Partition the tables by a column containing a TIMESTAMP or DATE Type.
- D. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
- E. Re-create the tables using DD
- F. Write an Apache Beam pipeline that creates a BigQuery table per data
Answer: B
NEW QUESTION # 52
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. HTTPS
- B. SOCKS
- C. VPN
- D. HTTP
Answer: B
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 # 53
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. Consume the stream of data in Cloud Dataflow using Kafka I
- B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- C. If that number falls below 4000, send an alert.
- D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- E. 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.
- F. Set a sliding time window of 1 hour every 5 minute
- G. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- I. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- K. Consume the stream of data in Cloud Dataflow using Kafka I
- L. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
- M. If that number falls below 4000, send an alert.
Answer: I
NEW QUESTION # 54
Cloud Dataproc is a managed Apache Hadoop and Apache service.
- A. Blaze
- B. Spark
- C. Ignite
- D. Fire
Answer: B
Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/
NEW QUESTION # 55
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