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Achieving the Google Certified Professional Data Engineer certification demonstrates to employers and colleagues that an individual has the knowledge and skills necessary to design and build data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification can open up new career opportunities and help individuals advance in their current roles.

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;

Google Certified Professional Data Engineer Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

A. multi-keyed data with very high latency
B. single-keyed data with very low latency
C. multi-keyed data with very low latency
D. single-keyed data with very high latency

Answer: B

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.

NEW QUESTION # 35
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

A. Use a row key of the form <timestamp>#<sensorid>.
B. Use a row key of the form >#<sensorid>#<timestamp>.
C. Use a row key of the form <timestamp>.
D. Use a row key of the form <sensorid>.

Answer: C

NEW QUESTION # 36
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 field, and partition by username field.
B. Partition the table by country and username fields.
C. Partition the table by _PARTITIONTIME.
D. Cluster the table by country and username fields

Answer: D

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 # 37
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

A. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
B. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
C. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
D. Disable external IP addresses from worker VMs and enable Private Google Access.

Answer: D

Explanation:
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. References:
* Private Google Access overview
* Using Private Google Access and Cloud NAT
* Using custom containers with Dataflow

NEW QUESTION # 38
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
B. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
D. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Answer: C

Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.

NEW QUESTION # 39
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