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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data
Topic 2: Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing
Topic 3: Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring - Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data
Topic 4: Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection - Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance
Topic 5: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation

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Google Certified Professional Data Engineer Exam Sample Questions (Q392-Q397):

NEW QUESTION # 392
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Convert all daily log tables into date-partitioned tables
B. Create separate views to cover each month, and query from these views
C. Enable query caching so you can cache data from previous months
D. Convert the sharded tables into a single partitioned table

Answer: A

Explanation:
Explanation

NEW QUESTION # 393
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. BigQuery cannot be accessed from local machines
B. Your gcloud does not have access to the BigQuery resources
C. You are missing gcloud on your machine
D. Pipelines cannot be run locally

Answer: B

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink

NEW QUESTION # 394
Which is not a valid reason for poor Cloud Bigtable performance?

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

Answer: D

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.

NEW QUESTION # 395
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Increase the number of nodes in the Cloud Bigtable cluster
B. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
C. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
D. Configure your Cloud Dataflow pipeline to use local execution
E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Answer: A,B

Explanation:
References:

NEW QUESTION # 396
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

A. You will not use the data to back a user-facing or latency-sensitive application.
B. You expect to store at least 10 TB of data.
C. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
D. You need to integrate with Google BigQuery.

Answer: D

Explanation:
Explanation
For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd

NEW QUESTION # 397
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