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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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. Processing logic
  • 2. Batch processing
  • 3. AI data enrichment
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Data acquisition and import
  • 6. Integrating with new data sources
- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
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
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Analyzing data access patterns
  • 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
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Mapping business requirements
  • 3. Deciding the degree of data normalization
  • 4. Designing the data model
Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Performing security considerations
  • 1. Data encryption
  • 2. Identity and Access Management (IAM)
  • 3. Auditing, privacy, and compliance
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
Maintaining and automating data workloads (~15% of the exam)15%- 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
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
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

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Google Certified Professional Data Engineer Exam Sample Questions (Q346-Q351):

NEW QUESTION # 346
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

Answer: B


NEW QUESTION # 347
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

Answer: C

Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms


NEW QUESTION # 348
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

Answer: A


NEW QUESTION # 349
You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

Answer: B

Explanation:
The requirements are:
* Explore ORC formatted files with Hive partitioning.
* Mimic the SQL on Hive query engine experience.
* Cost-effective storage and processing.
* Avoid impacting the on-premises Hadoop solution.
Let's analyze the options:
* Option A (Import to Bigtable):Bigtable is a NoSQL database, not suited for SQL-based exploration of ORC files or Hive-style partitioning directly. This would require significant data transformation and a different query paradigm. Not cost-effective for this use case.
* Option B (Import to BigQuery native tables):Importing data into BigQuery native storage is an option. BigQuery can load ORC files. This provides excellent query performance. However, it involves an ETL step (importing) and storage costs for the datawithin BigQuery, which might be higher than keeping it in its original format on Cloud Storage if query patterns are exploratory and not extremely frequent on all data.
* Option C (Copy to Cloud Storage, deploy Dataproc):Dataproc allows you to run Hadoop/Spark (and thus Hive) clusters on Google Cloud. This would provide a very similar experience ("SQL on the Hive query engine"). However, running a persistent Dataproc cluster incurs compute costs for the cluster nodes, even when not actively querying. While ephemeral clusters are possible, it adds operational overhead for exploratory queries. Storage on Cloud Storage is cost-effective.
* Option D (Copy to Cloud Storage, create external BigQuery tables):This is often the most cost- effective and straightforward solution for this scenario.
* Cost-effective Storage:Cloud Storage is a low-cost option for storing files like ORC.
* SQL Interface:BigQuery provides a familiar SQL interface.
* External Tables:BigQuery can query data directly from Cloud Storage (including ORC files) using external tables. This avoids the need to load data into BigQuery's managed storage, saving on storage costs and ETL effort.
* Hive Partitioning:BigQuery external tables support Hive partitioning layouts. When you define the external table, you can specify the partitioning scheme, and BigQuery will use partition pruning to scan only relevant partitions, improving performance and reducing costs for queries that filter on partition keys. This directly mimics the Hive experience.
* Processing Cost:You only pay for the data scanned by BigQuery queries, which aligns with exploratory analysis.
Comparing D with B: External tables are generally more cost-effective for storage and initial setup if the data is already in ORC and an ETL process into BigQuery native storage is to be avoided. Query performance might be slightly less than native tables but is often sufficient for exploration, especially with partitioning.
Comparing D with C: BigQuery external tables are serverless, meaning no cluster to manage or pay for when idle. Dataproc requires managing and paying for a cluster. For exploration, the serverless nature of BigQuery is usually more cost-effective.
Therefore, copying ORC files to Cloud Storage and using BigQuery external tables is the most cost-effective solution that meets all requirements.
Reference:
Google Cloud Documentation: BigQuery > External data sources > Querying Cloud Storage data. "You can query data in Cloud Storage by using external tables or federated queries. External tables are tables that read data directly from files in Cloud Storage." Google Cloud Documentation: BigQuery > External data sources > Supported formats and compression types. ORC is a supported format.
Google Cloud Documentation: BigQuery > Creating and using tables > Creating external tables. "External tables let you query data stored in Cloud Storage as if it were a standardBigQuery table. You can use external tables to query data in various formats, including... ORC..." Google Cloud Documentation: BigQuery > Creating and using tables > Querying partitioned external tables.
"You can create an external table that is partitioned on Hive partitioning keys. When you query a Hive partitioned external table, BigQuery performs partition pruning to skip reading unnecessary partitions." This directly addresses the "Hive partitioning" and "explore data in a similar way" requirements.
Google Cloud Blog: "Choosing the right data processing option on GCP: BigQuery vs. Dataproc" (and similar articles) often highlight BigQuery external tables as a cost-effective way to query data in place on Cloud Storage, especially for data lake scenarios.


NEW QUESTION # 350
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

Answer: C


NEW QUESTION # 351
......

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