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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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 - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle - Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption
Storing the data (~20% of the exam)	20%	- 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. Designing the data model 4. Mapping business requirements - 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. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data
Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Scheduling jobs 3. Workflow orchestration - Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Performing data quality and validation checks 3. Recovering from failures
Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cluster sizing and autoscaling 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cost optimization - Designing data pipelines 1. Processing logic 2. Integrating with new data sources 3. Data acquisition and import 4. AI data enrichment 5. Streaming (e.g., windowing, late arriving data) 6. Batch processing - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements

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Google Certified Professional Data Engineer Exam Sample Questions (Q330-Q335):

NEW QUESTION # 330
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
B. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.
C. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
D. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.

Answer: B

Explanation:
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them. You can then load the de-identified data into a BigQuery table for analysis. Reference:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

NEW QUESTION # 331
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

A. 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.
B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
C. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
D. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.

Answer: D

Explanation:
Bigquery is used to analyse access logs, data access logs capture the details of the user that accessed the data.

NEW QUESTION # 332
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

A. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
B. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
C. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
D. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
E. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

Answer: B,C

NEW QUESTION # 333
You want to migrate your existing Teradata data warehouse to BigQuery. You want to move the historical data to BigQuery by using the most efficient method that requires the least amount of programming, but local storage space on your existing data warehouse is limited. What should you do?

A. Use BigQuery Data Transfer Service by using the Java Database Connectivity (JDBC) driver with FastExport connection.
B. Use BigQuery Data Transfer Service with the Teradata Parallel Transporter (TPT) tbuild utility.
C. Create a Teradata Parallel Transporter (TPT) export script to export the historical data, and import to BigQuery by using the bq command-line tool.
D. Create a script to export the historical data, and upload in batches to Cloud Storage. Set up a BigQuery Data Transfer Service instance from Cloud Storage to BigQuery.

Answer: A

NEW QUESTION # 334
Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results.
However, the dashboard is slow to show the results when applying the filters to the following query:

How should you redesign the BigQuery table to support faster access?

A. Cluster the table by country and username fields
B. Partition the table by _PARTITIONTIME.
C. Cluster the table by country field, and partition by username field.
D. Partition the table by country and username fields.

Answer: A

Explanation:
To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:
* Clustering in BigQuery:
* Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.
* Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.
* Filter Efficiency:
* With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.
* This directly addresses the performance issue of the dashboard queries that apply filters on these fields.
Steps to Implement:
* Redesign the Table:
* Create a new table with clustering on countryname and username:
CREATE TABLE project.dataset.new_table
CLUSTER BY countryname, username AS
SELECT * FROM project.dataset.customer_order;
* Migrate Data:
* Transfer the existing data from the original table to the new clustered table.
* Update Queries:
* Modify the dashboard queries to reference the new clustered table.
Reference Links:
* BigQuery Clustering Documentation
* Optimizing Query Performance

NEW QUESTION # 335
......

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