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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Storing the data (~20% of the exam)	20%	- Selecting storage systems 1. Planning for storage costs and performance 2. Lifecycle management of data 3. Analyzing data access patterns - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Mapping business requirements 3. Designing the data model 4. Deciding the degree of data normalization - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data - Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems
Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD) - Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting
Designing data processing systems (~30% of the exam)	30%	- Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB - Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cost optimization 3. Cluster sizing and autoscaling - Designing data pipelines 1. Processing logic 2. AI data enrichment 3. Batch processing 4. Data acquisition and import 5. Streaming (e.g., windowing, late arriving data) 6. Integrating with new data sources
Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration - Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools
Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines - Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases

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Google Certified Professional Data Engineer Exam Sample Questions (Q362-Q367):

NEW QUESTION # 362
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

A. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
B. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
C. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
D. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: B

Explanation:
Explanation/Reference:

NEW QUESTION # 363
What is the HBase Shell for Cloud Bigtable?

A. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.
B. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
D. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

Answer: D

Explanation:
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell

NEW QUESTION # 364
You have a variety of files in Cloud Storage that your data science team wants to use in their models. Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

A. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
B. Load the data into BigQuery and use SQL to transform the data as necessary. Provide the data science team access to staging tables to explore the raw data.
C. Create an external table in BigQuery and use SQL to transform the data as necessary. Provide the data science team access to the external tables to explore the raw data.
D. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.

Answer: A

NEW QUESTION # 365
You created a new version of a Dataflow streaming data ingestion pipeline that reads from Pub/Sub and writes to BigQuery. The previous version of the pipeline that runs in production uses a 5-minute window for processing. You need to deploy the new version of the pipeline without losing any data, creating inconsistencies, or increasing the processing latency by more than 10 minutes. What should you do?

A. Update the old pipeline with the new pipeline code.
B. Snapshot the old pipeline, stop the old pipeline, and then start the new pipeline from the snapshot.
C. Drain the old pipeline, then start the new pipeline.
D. Cancel the old pipeline, then start the new pipeline.

Answer: C

NEW QUESTION # 366
You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

A. 1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Set a locked retention policy on the bucket.
3. Create a BigQuery external table on the exported files.
B. 1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Enable versionmg on the bucket.
3. Create a BigQuery external table on the exported files.
C. 1 Create a BigQuery table snapshot.
2 Restore the snapshot when you need to perform analytics.
D. 1 Create a BigQuery table clone.
2. Query the clone when you need to perform analytics.

Answer: A

Explanation:
This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. Reference:
Exporting table data | BigQuery | Google Cloud
Storage classes | Cloud Storage | Google Cloud
Retention policies and retention periods | Cloud Storage | Google Cloud Federated queries | BigQuery | Google Cloud

NEW QUESTION # 367
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