Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) Free Practice Test

Question 1

Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

A. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.

B. Migrate the workloads to Dataproc plus Cloud Storage; modernize later.

C. Migrate the workloads to Dataproc plus HDFS; modernize later.

D. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery.

Correct Answer: B

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Question 2

To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

A. gcloud ml-engine jobs submit training

B. gcloud ml-engine local train

C. You can't run a TensorFlow program on your own computer using Cloud ML Engine .

D. gcloud ml-engine jobs submit training local

Correct Answer: B

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Question 3

You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

A. BigQuery cannot be used as a sink

B. Only batch

C. Both batch and streaming

D. Only streaming

Correct Answer: C

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Question 4

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

A. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.

B. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.

C. Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

D. Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

E. Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.

Correct Answer: D,E

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Question 5

You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center. Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time. What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

A. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates

B. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates

C. gsutil for both the migration and the real-time updates

D. BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real-time updates

Correct Answer: B

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Question 6

You are developing a fraud detection model using BigQuery ML. You have a raw transaction dataset and need to create new features such as the average_transaction_amount_last_24_hours and time_since_last_transaction. These features require aggregation and time-window calculations on the existing data. The goal is to ensure that these features are consistently applied during both model training and prediction without manual intervention. You need to prepare these features efficiently for your model. What should you do?

A. Create a separate BigQuery table containing pre-computed features using complex SQL queries and join this table with the raw data during model training and serving.

B. Implement a Cloud Run function that triggers on new transactions, calculates the features, and inserts them into a feature store before model serving.

C. Export the BigQuery data to Cloud Storage, perform feature engineering using a custom Python script in a Dataflow job, and then re-import the engineered features into BigQuery.

D. Use the TRANSFORM clause within the CREATE MODEL statement, leveraging SQL functions for aggregations and time-based calculations.

Correct Answer: A

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Question 7

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules.
You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.

C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Correct Answer: D

Question 8

You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?

A. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.

B. Use BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.

C. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.

D. Use BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.

Correct Answer: C

Question 9

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Use at least 300 GB of data.

B. Do not use a production instance.

C. Before you test, run a heavy pre-test for several minutes.

D. Run your test for at least 10 minutes.

Correct Answer: B

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Question 10

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?

A. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.

B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.

C. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.

Correct Answer: C

Question 11

By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 100 MB of data

B. Single, Global Window

C. Windows at every 1 minute

D. Windows at every 10 minutes

Correct Answer: B

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Question 12

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

A. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

B. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

C. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

D. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

Correct Answer: D

Question 13

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

A. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

B. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

D. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Correct Answer: A

Question 14

Which of the following is not true about Dataflow pipelines?

A. Pipelines represent a directed graph of steps

B. Pipelines are a set of operations

C. Pipelines represent a data processing job

D. Pipelines can share data between instances

Correct Answer: D

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Google Certified Professional Data Engineer (Professional-Data-Engineer) Free Practice Test