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| Section | Weight | Objectives |
|---|
| Topic 1: Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Build ETL and ELT pipelines
- 2. Optimize pipeline performance and cost
- 3. Design orchestration workflows
- Designing for data ingestion
- 1. Design batch and streaming ingestion solutions
- 2. Select appropriate storage systems
- 3. Design scalable and reliable ingestion architecture
|
| Topic 2: Ensuring solution quality | 20%-25% | - Data quality management
- 1. Validate and clean data
- 2. Implement data governance
- 3. Maintain metadata and lineage
- Security and compliance
- 1. Implement encryption and compliance controls
- 2. Protect sensitive data
- 3. Manage IAM and access control
|
| Topic 3: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Design highly available systems
- 3. Scale data workloads efficiently
- Managing resources and costs
- 1. Control operational costs
- 2. Tune query and pipeline performance
- 3. Optimize storage and compute usage
|
| Topic 4: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Implement batch processing systems
- 2. Implement streaming data systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
- 1. Implement fault tolerance and recovery
- 2. Monitor data pipelines and workloads
- 3. Manage logging and alerting
|
>> Exam GCP-DE Preview <<
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Google Data Engineer Sample Questions (Q69-Q74):
NEW QUESTION # 69
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: dateColumn data: device_id, data_point
- B. Rowkey: date#device_idColumn data: data_point
- C. Rowkey: device_idColumn data: date, data_point
- D. Rowkey: data_pointColumn data: device_id, date
- E. Rowkey: date#data_pointColumn data: device_id
Answer: D
NEW QUESTION # 70
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
- B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
- C. Dataflow pipelines can consume data from other Google Cloud services
- D. Dataflow pipelines can be programmed in Java
Answer: A
Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 71
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store the data in a file in a regional Google Cloud Storage bucke
- B. Load the data every 30 minutes into a new partitioned table in BigQuery.
- C. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- D. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- E. Store the data in Google Cloud Datastor
- F. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
Answer: B
NEW QUESTION # 72
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
- A. A sequential numeric ID
- B. A timestamp followed by a stock symbol
- C. A non-sequential numeric ID
- D. A stock symbol followed by a timestamp
Answer: A,B
Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti
NEW QUESTION # 73
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?
- A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
- B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
- C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
- D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
Answer: A
NEW QUESTION # 74
......
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