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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q49-Q54):

NEW QUESTION # 49
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 Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B. 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
C. 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
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Answer: B

NEW QUESTION # 50
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 51
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Merge the transformed tables together with a SQL query

Answer: B

NEW QUESTION # 52
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Answer: A

NEW QUESTION # 53
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can consume data from other Google Cloud services
B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines can be programmed in Java

Answer: C

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 # 54
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