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- Google Professional-Data-Engineer Exam Syllabus Topics:
- Section Weight Objectives
- Designing data processing systems 20% - Designing for regulatory and security requirements
- Ensuring data privacy and compliance
- Implementing access control and data protection
- Designing for business requirements
- Designing for scalability and elasticity
- Selecting appropriate storage solutions
- Designing for reliability and fault tolerance
- Building and operationalizing data processing systems 25% - Deploying and managing systems
- Managing infrastructure and resources
- Monitoring and logging data processes
- Building data pipelines
- Ingesting data from various sources
- Transforming and cleaning data
- Orchestrating data workflows
- Ensuring solution quality and reliability 17% - Testing and validating data systems
- Data quality validation
- Performance and scalability testing
- Troubleshooting and optimization
- Diagnosing performance issues
- Optimizing queries and workloads
- Operationalizing machine learning models 20% - Deploying and maintaining ML models
- Optimizing model performance and cost
- Model serving and monitoring
- Preparing data for ML
- Handling structured and unstructured data
- Feature engineering and data preparation
- Maintaining and automating data workloads 18% - Automation and repeatability
- Implementing CI/CD for data systems
- Automating deployment and updates
- Resource optimization
- Choosing appropriate compute and storage options
- Cost management and resource allocation
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- Google Certified Professional Data Engineer Exam Sample Questions (Q301-Q306):
- NEW QUESTION # 301
- Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
- The old data can be deleted anytime
- You plan to use the visualization layer for current and historical reporting
- The old data should be available instantly when accessed
- There should not be any charges for data retrieval.
- What should you do to optimize for cost?
- A. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.
- B. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.
- C. Create the bucket with the Autoclass storage class feature.
- D. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.
- Answer: C
- Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.
- NEW QUESTION # 302
- After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
- What should you do?
- A. Select random samples from the tables using the HASH() function and compare the samples.
- B. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.
- C. Select random samples from the tables using the RAND() function and compare the samples.
- D. Create stratified random samples using the OVER() function and compare equivalent samples from each table.
- Answer: B
- Explanation:
- Full comparison with this option, rest are comparison on sample which doesn't ensure all the data will be ok.
- NEW QUESTION # 303
- What are two of the benefits of using denormalized data structures in BigQuery?
- A. Increases query speed, makes queries simpler
- B. Reduces the amount of data processed, reduces the amount of storage required
- C. Reduces the amount of data processed, increases query speed
- D. Reduces the amount of storage required, increases query speed
- Answer: A
- Explanation:
- Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
- Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
- Reference:
- https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
- NEW QUESTION # 304
- Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
- What should you do?
- A. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
- B. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- C. Add a try... catch block to your sideOutput to create a PCollection that can be stored to PubSub later.
- D. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- Answer: A
- NEW QUESTION # 305
- Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?
- A. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
- B. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
- C. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
- D. Create a Google Cloud Dataflow job to process the data.
- E. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
- Answer: A
- Explanation:
- Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.
- NEW QUESTION # 306
- .....
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