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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement streaming data systems 2. Implement batch processing 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
Topic 2: Ensuring solution quality	20%-25%	- Security and compliance 1. Protect sensitive data 2. Implement encryption and compliance controls 3. Manage IAM and access control - Data quality management 1. Maintain metadata and lineage 2. Implement data governance 3. Validate and clean data
Topic 3: Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Scale data workloads efficiently 2. Implement disaster recovery strategies 3. Design highly available systems - Managing resources and costs 1. Optimize storage and compute usage 2. Control operational costs 3. Tune query and pipeline performance
Topic 4: 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 batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
D. Use Cloud Deployment Manager to automate access provision.
E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: A,F

NEW QUESTION # 67
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that provides an AP
B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
C. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
D. Create an application that writes to a Cloud SQL database to store the dat
E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
F. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Answer: A

NEW QUESTION # 68
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: device_idColumn data: date, data_point
C. Rowkey: date#data_pointColumn data: device_id
D. Rowkey: date#device_idColumn data: data_point
E. Rowkey: data_pointColumn data: device_id, date

Answer: E

NEW QUESTION # 69
Which is not a valid reason for poor Cloud Bigtable performance?

A. The workload isn't appropriate for Cloud Bigtable.
B. The table's schema is not designed correctly.
C. The Cloud Bigtable cluster has too many nodes.
D. There are issues with the network connection.

Answer: C

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 70
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. 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
B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E. Merge the transformed tables together with a SQL query
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Answer: F

NEW QUESTION # 71
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