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Official Professional-Data-Engineer Practice Test - Google Google Certified Professional Data Engineer Exam - High Pass-Rate Professional-Data-Engineer Exam Topics

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

Designing and building data processing systems on the Google Cloud Platform
Predicting machine models using TensorFlow and Cloud ML
Influencing unstructured data using ML APIs on Cloud Dataproc
Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
Enable insights from streaming data

Google Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.
B. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors.
Configure your BigQuery ML model to use the "ingestion' dataset as the training data.
C. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.
D. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

Answer: A

Explanation:
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

NEW QUESTION # 18
You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?

A. Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
B. Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.
C. Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
D. Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.

Answer: C

Explanation:
Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.
By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.
Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.
Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.
Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. Reference: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastream quickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.

NEW QUESTION # 19
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
B. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
C. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
D. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
E. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Answer: E

NEW QUESTION # 20
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

A. Use your model to run predictions on fresh customer input data.
B. Delineate what data will be used for testing and what will be used for training the model.
C. Test and evaluate your model on your curated data to determine how well the model performs.
D. Monitor your model performance, and make any adjustments needed.

Answer: B

Explanation:
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a productionenvironment. References: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle

NEW QUESTION # 21
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of dat
a. They want to improve this performance while minimizing cost. What should they do?

A. Redefine the schema by evenly distributing reads and writes across the row space of the table.
B. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Answer: A

NEW QUESTION # 22
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