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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Storing the data (~20% of the exam)	20%	- 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 - 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. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Data acquisition and import 2. Streaming (e.g., windowing, late arriving data) 3. AI data enrichment 4. Integrating with new data sources 5. Batch processing 6. Processing logic - 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. Cluster sizing and autoscaling 2. Cost optimization 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Identity and Access Management (IAM) 2. Data encryption 3. Auditing, privacy, and compliance - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines
Topic 4: Maintaining and automating data workloads (~15% of the exam)	15%	- Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs - 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
Topic 5: 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. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools

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Google Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

A. Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
B. Schedule a daily copy of the dataset to a backup region.
C. Schedule a daily BigQuery snapshot of the table.
D. Modify ETL job to load the data into both the current and another backup region.

Answer: C

Explanation:
To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
* Cloud Functions:
* Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
* This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
* Flexibility and Simplicity:
* Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
* Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
* Performance:
* Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
* They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
Steps to Implement:
* Write the Cloud Function:
* Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
* Deploy the function to Google Cloud.
* Invoke Cloud Function from Workflow:
* Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
steps:
- callCloudFunction:
call: http.post
args:
url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
body:
key: value
* Process Results:
* Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
Reference Links:
* Google Cloud Functions Documentation
* Using Workflows with Cloud Functions
* Workflows Standard Library

NEW QUESTION # 98
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

A. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
C. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
D. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

Answer: D

Explanation:
https://cloud.google.com/iam/docs/roles-audit-logging#scenario_external_auditors

NEW QUESTION # 99
You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.
B. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Schedule the DAGs to run hourly.
C. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.
D. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Schedule the DAG to run hourly.

Answer: C

NEW QUESTION # 100
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query
B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
C. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Answer: D

NEW QUESTION # 101
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

A. Enable the Object Versioning feature. Create a copy in a bucket in a different region.
B. Set a retention policy. Lock the retention policy.
C. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
D. Enable the Object Versioning feature. Add a lifecycle rule.

Answer: B

NEW QUESTION # 102
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