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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection
Topic 2: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. Data encryption and compliance 2. IAM and access control
Topic 4: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion

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Google Data Engineer Sample Questions (Q20-Q25):

NEW QUESTION # 20
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Use at least 300 GB of data.
B. Before you test, run a heavy pre-test for several minutes.
C. Run your test for at least 10 minutes.
D. Do not use a production instance.

Answer: D

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 21
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. HiveQL using Hive
B. PigLatin using Pig
C. Java using MapReduce
D. Python using MapReduce

Answer: D

NEW QUESTION # 22
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. 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. Merge the transformed tables together with a SQL query
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme

Answer: B

NEW QUESTION # 23
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. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
B. Use Cloud Deployment Manager to automate access provision.
C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,D

NEW QUESTION # 24
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create a service account and grant dataset access to that accoun
B. Create groups for your users and give those groups access to the dataset
C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
D. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
E. Use the service account's private key to access the dataset
F. Create a dummy user and grant dataset access to that use

Answer: A

NEW QUESTION # 25
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