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| Section | Weight | Objectives |
|---|
| Topic 1: Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
- 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
- 2. Mapping storage options to business requirements
- Designing data pipelines
- 1. Data acquisition and import
- 2. Processing logic
- 3. Streaming (e.g., windowing, late arriving data)
- 4. AI data enrichment
- 5. Integrating with new data sources
- 6. Batch processing
- Designing data processing resources
- 1. Cluster sizing and autoscaling
- 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- 3. Cost optimization
|
| Topic 2: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
- 1. Preparing data for reporting and dashboards
- 2. Connecting to Looker and other BI tools
- Sharing data securely
- 1. Data sharing and collaboration
- 2. Publishing datasets
|
| Topic 3: Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
- 1. Performing data quality and validation checks
- 2. Planning for monitoring and alerting
- 3. Recovering from failures
- Monitoring data pipelines and data processes
- 1. Managing quotas and resource usage
- 2. Logging, monitoring, and troubleshooting
- Automating data processes
- 1. Workflow orchestration
- 2. Continuous integration and continuous deployment (CI/CD)
- 3. Scheduling jobs
|
| Topic 4: Storing the data (~20% of the exam) | 20% | - Using a data lake
- 1. Monitoring the data lake
- 2. Managing the lake (data discovery, access, cost controls)
- 3. Processing data
- Planning for using a data warehouse
- 1. Mapping business requirements
- 2. Defining architecture to support data access patterns
- 3. Designing the data model
- 4. Deciding the degree of data normalization
- 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
- Selecting storage systems
- 1. Planning for storage costs and performance
- 2. Analyzing data access patterns
- 3. Lifecycle management of data
|
| Topic 5: Ingesting and processing the data (~20% of the exam) | 20% | - Performing security considerations
- 1. Data encryption
- 2. Auditing, privacy, and compliance
- 3. Identity and Access Management (IAM)
- Building and maintaining data structures and databases
- 1. Defining data lifecycle
- 2. Planning for analytical and operational use cases
- Deploying and operationalizing the pipelines
- 1. Job automation and orchestration (Cloud Composer, Workflows)
- 2. CI/CD for data pipelines
|
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Google Certified Professional Data Engineer Exam Sample Questions (Q387-Q392):
NEW QUESTION # 387
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
- A. categorical_column_with_unknown_values
- B. categorical_column_with_vocabulary_list
- C. categorical_column_with_hash_bucket
- D. sparse_column_with_keys
Answer: C
Explanation:
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance?
Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference:
https://www.tensorflow.org/tutorials/wide
NEW QUESTION # 388
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
- A. Hashing
- B. Salting
- C. Randomization
- D. Field promotion
Answer: D
Explanation:
Explanation
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti
NEW QUESTION # 389
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?
- A. 1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
- B. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.3. Assign permissions to the data engineering teams at the zone level.
- C. 1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
- D. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.
2 Provide each data engineering team access to the virtual lake.
Answer: A
Explanation:
This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12. By creating a Dataplex virtual lake for each data product,you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3. By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.
Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2. Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34. Option C is better than option A and B, but it does not create multiple zones forlanding, raw, and curated data, which may compromise the security and quality of the data products5. References:
1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog
2: Data Mesh - 7 Effective Practices to Get Started - Confluent
3: Best practices | Dataplex | Google Cloud
4: Secure your lake | Dataplex | Google Cloud
5: Zones | Dataplex | Google Cloud
[6]: Managing a Data Mesh with Dataplex - ROI Training
NEW QUESTION # 390
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

- A. Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes
- B. Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery
- C. Create a Memorystore instance with a high availability (HA) configuration
Answer: A
Explanation:
D Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas
NEW QUESTION # 391
Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time.
Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Google Cloud Datastore
- B. Cloud Bigtable
- C. Google Cloud Storage
- D. Google BigQuery
Answer: B
Explanation:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION # 392
......
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