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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Answer: C

NEW QUESTION # 34
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: device_idColumn data: date, data_point
B. Rowkey: date#device_idColumn data: data_point
C. Rowkey: data_pointColumn data: device_id, date
D. Rowkey: dateColumn data: device_id, data_point
E. Rowkey: date#data_pointColumn data: device_id

Answer: C

NEW QUESTION # 35
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. An hourly watermark
B. A processing time trigger
C. The with Allowed Lateness method
D. An event time trigger

Answer: B

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

NEW QUESTION # 36
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Functions
B. Cloud Dataflow
C. Cloud Scheduler
D. Cloud Composer

Answer: C

NEW QUESTION # 37
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element size in bytes
B. Trigger based on time
C. Trigger that is a combination of other triggers
D. Trigger based on element count

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 38
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