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| Section | Weight | Objectives |
|---|
| Topic 1: 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
- Designing for reliability and fidelity
- 1. Performing data quality and validation checks
- 2. Recovering from failures
- 3. Planning for monitoring and alerting
- Automating data processes
- 1. Continuous integration and continuous deployment (CI/CD)
- 2. Workflow orchestration
- 3. Scheduling jobs
|
| Topic 2: Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
- 1. Data sharing and collaboration
- 2. Publishing datasets
- Preparing data for visualization
- 1. Connecting to Looker and other BI tools
- 2. Preparing data for reporting and dashboards
|
| Topic 3: 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. AI data enrichment
- 2. Data acquisition and import
- 3. Streaming (e.g., windowing, late arriving data)
- 4. Processing logic
- 5. Batch processing
- 6. Integrating with new data sources
- Designing data processing resources
- 1. Cluster sizing and autoscaling
- 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- 3. Cost optimization
|
| Topic 4: Storing the data (~20% of the exam) | 20% | - Designing for a data platform
- 1. Building a federated governance model for distributed data systems
- 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Planning for using a data warehouse
- 1. Deciding the degree of data normalization
- 2. Defining architecture to support data access patterns
- 3. Mapping business requirements
- 4. Designing the data model
- Selecting storage systems
- 1. Analyzing data access patterns
- 2. Planning for storage costs and performance
- 3. Lifecycle management of data
- Using a data lake
- 1. Processing data
- 2. Managing the lake (data discovery, access, cost controls)
- 3. Monitoring the data lake
|
| Topic 5: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
- 1. Defining data lifecycle
- 2. Planning for analytical and operational use cases
- Performing security considerations
- 1. Auditing, privacy, and compliance
- 2. Data encryption
- 3. Identity and Access Management (IAM)
- 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 (Q36-Q41):
NEW QUESTION # 36
You need to create a new transaction table in Cloud Spanner that stores product sales dat
a. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?
- A. The current epoch time
- B. The original order identification number from the sales system, which is a monotonically increasing integer
- C. A random universally unique identifier number (version 4 UUID)
- D. A concatenation of the product name and the current epoch time
Answer: C
Explanation:
References:
NEW QUESTION # 37
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?
- A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
- B. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- C. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: B
Explanation:
Spanner allows transaction tables to scale horizontally and secondary indexes for range queries.
NEW QUESTION # 38
You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?
- A. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
- B. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage
- C. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in
Cloud Storage - D. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.
Answer: B
NEW QUESTION # 39
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat A.
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
- A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
- B. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
- C. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
- D. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
Answer: A
NEW QUESTION # 40
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.
To do this you need to add a synthetic feature. What should the value of that feature be?