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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment - Planning data solutions 1. Planning for data security and compliance 2. Selecting appropriate storage solutions 3. Designing for reliability, scalability, and efficiency
Topic 2: Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Topic 3: Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
Topic 4: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Optimizing query performance 2. Implementing data sharing and governance 3. Preparing datasets for querying and reporting
Topic 5: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs

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GCP-DE Valid Test Guide - GCP-DE New Study Notes

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Google Data Engineer Sample Questions (Q42-Q47):

NEW QUESTION # 42
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. Merge the transformed tables together with a SQL query
B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C. 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
D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Answer: F

NEW QUESTION # 43
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: dateColumn data: device_id, data_point
D. Rowkey: data_pointColumn data: device_id, date
E. Rowkey: date#data_pointColumn data: device_id

Answer: D

NEW QUESTION # 44
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. Java using MapReduce
B. HiveQL using Hive
C. PigLatin using Pig
D. Python using MapReduce

Answer: D

NEW QUESTION # 45
Which of the following statements is NOT true regarding Bigtable access roles?

A. You can configure access control only at the project level.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
D. To give a user access to only one table in a project, you must configure access through your application.

Answer: C

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 46
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. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
B. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Answer: D

NEW QUESTION # 47
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