Google Cloud Professional Data Engineer Exam Dumps 2025
Google Cloud Professional Data Engineer Exam Dumps 2025
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Question 1:
A new workload has been deployed to Cloud Dataproc, which is configured with an autoscaling policy. You are noticing a FetchFailedException is occurring intermittently. What would be the most likely cause of this problem?
A. The autoscaling policy is adding nodes too fast and data is being dropped.
B. You are using a GCS bucket with improper access controls.
C. You are using Google Cloud Storage instead of local storage for persistent storage.
D. The autoscaling policy is scaling down and shuffle data is lost when a node is decommissioned.
Answer: D
Explanation:
The FetchFailedException can occur when shuffle data is lost when a node is decommissioned. The autopolicy should be configured based on the longest running job in the cluster. Adding nodes will not cause a loss of data. Cloud Storage is the preferred persistent storage method for Dataproc clusters. While FetchFailedException can be caused by network issues, that is not likely to be a problem when using Cloud Storage for a Cloud Dataproc cluster. If the storage bucket had improper access controls then errors would occur consistently, not intermittently. See https://cloud.google.com/blog/topics/developers-practitioners/dataproc-best-practices-guide
Question 2:
Scenario: You are tasked with selecting a database for a new project that must meet the following criteria: fully managed, capable of automatic scaling, transactionally consistent, able to scale up to 6 TB, and queryable using SQL.
Which database would you recommend for this project?
A. Cloud SQL
B. Cloud Bigtable
C. Cloud Spanner
D. Cloud Datastore
Answer: C
Explanation:
Fully Managed: Cloud Spanner is a fully managed, globally distributed, and scalable database service provided by Google Cloud.
Automatic Scaling: Cloud Spanner automatically scales horizontally to handle increased workloads and data volumes without requiring manual intervention.
Transactional Consistency: Cloud Spanner provides strong transactional consistency, ensuring that all data modifications are processed in the correct order and that data remains consistent across all regions.
Scalability: Cloud Spanner can easily scale up to and beyond 6 TB while maintaining performance and consistency.
SQL Queries: Cloud Spanner supports SQL queries, making it compatible with existing SQL-based analytics and applications.
While other options like Cloud SQL might seem relevant, they have limitations:
Cloud SQL: While fully managed and supporting SQL, Cloud SQL’s scalability might be limited for a 6 TB database. It might require manual scaling or sharding, which can be complex.
Cloud Bigtable: Not designed for SQL queries and primarily focuses on NoSQL data storage.
Cloud Datastore: A NoSQL document database, not suitable for SQL-based queries.
Question 3:
Scenario: Your team is currently tackling a binary classification problem. You have utilized a support vector machine (SVM) classifier with default parameters and achieved an area under the Curve (AUC) of 0.87 on the validation set.
To enhance the AUC of the model, what steps should you take next?
A. Perform hyperparameter tuning
B. Train a classifier with deep neural networks, because neural networks would always beat SVMs
C. Deploy the model and measure the real-world AUC; it‘s always higher because of generalization
D. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
Answer: A
Explanation:
Hyperparameter Tuning: SVMs have several crucial hyperparameters that significantly influence their performance. These include:
C: The penalty parameter for misclassification.
Kernel: The type of kernel function (linear, RBF, polynomial, etc.).
Gamma: Kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’.
Degree: Degree for ‘poly’ kernel.
By systematically exploring different combinations of these hyperparameters, you can find the optimal settings that maximize the model’s AUC on the validation set. Techniques like grid search, random search, or Bayesian optimization can be used for efficient hyperparameter tuning.
Why other options are less suitable:
Train a classifier with deep neural networks: While deep neural networks can achieve excellent results in many cases, they are not guaranteed to outperform SVMs. They often require more data and computational resources to train effectively.
Deploy the model and measure the real-world AUC: Real-world performance might differ from validation set performance, but deploying the model without further optimization is not the best approach to improve AUC.
Scale predictions: Scaling predictions might help in specific cases, but it’s not a general strategy for improving model performance and is unlikely to significantly impact AUC.
By carefully tuning the SVM’s hyperparameters, you can likely achieve a substantial improvement in its AUC and overall performance.
Question 4:
Scenario: You have 2 PB of historical data stored in an on-premises storage appliance that needs to be moved to Cloud Storage within six months. Your outbound network capacity is limited to 20 Mb/sec.
What is the best approach to migrate this data to Cloud Storage considering the network constraints and timeframe?
A. Use Transfer Appliance to copy the data to Cloud Storage
B. Use gsutil cp ?“J to compress the content being uploaded to Cloud Storage
C. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
D. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic
Answer: A
Explanation:
Using Transfer Appliance is the best option for moving 2 PB of historical data within six months when the outbound network capacity is constrained to 20 Mb/sec. Transfer Appliance is a physical storage appliance provided by Google Cloud that allows you to securely transfer large amounts of data to Google Cloud Storage. You can load your data onto the appliance and then ship it to a Google Cloud data center for fast and secure data transfer.
Why the other options are incorrect:
B. Using gsutil cp with compression: While compression can help reduce the size of the data being transferred, it won‘t address the network capacity constraint. The compression process may also add extra processing overhead and time, which could be counterproductive in this scenario.
C. Creating a private URL and using Storage Transfer Service: This option involves transferring the data over the network, which is limited to 20 Mb/sec. It does not provide a solution to overcome the network capacity constraint within the required timeframe.
D. Using trickle or ionice with gsutil cp: While these tools can help limit the bandwidth utilization of gsutil, they may not be as effective in ensuring the timely transfer of 2 PB of data within six months. Additionally, managing bandwidth using these tools may require more manual intervention and monitoring compared to using Transfer Appliance.
Question 5:
Scenario: You are implementing several batch jobs with interdependent steps that need to be executed in a specific order. The jobs involve shell scripts, Hadoop jobs, and BigQuery queries, with run times ranging from minutes to hours. Failed steps must be retried a fixed number of times.
Which service is recommended for managing the execution of these jobs?
A.Cloud Scheduler
B. Cloud Dataflow
C. Cloud Functions
D. Cloud Composer
Answer: D
Explanation:
Cloud Composer is a fully managed workflow orchestration service that allows you to author, schedule, and monitor workflows. It is based on Apache Airflow, which is an open-source tool for orchestrating complex workflows. Cloud Composer is well-suited for managing batch jobs with interdependent steps that need to be executed in a specific order. It supports executing shell scripts, running Hadoop jobs, and running queries in BigQuery. Additionally, Cloud Composer allows you to define retries for each task in the workflow, ensuring that failed steps are retried a fixed number of times.
Now, let‘s explain why the other options are incorrect:
A. Cloud Scheduler: Cloud Scheduler is a fully managed cron job service that allows you to schedule jobs in the cloud. While it can be used for scheduling jobs, it does not provide the workflow orchestration capabilities needed to manage complex batch jobs with interdependent steps.
B. Cloud Dataflow: Cloud Dataflow is a fully managed service for processing and executing data pipelines. While it is great for processing large amounts of data in parallel, it is not specifically designed for managing batch jobs with complex dependencies and retries.
C. Cloud Functions: Cloud Functions is a serverless compute service that allows you to run single-purpose functions in response to events. While it is useful for event-driven tasks, it is not designed for orchestrating complex workflows with interdependent steps and retries.
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Question 6:
Scenario: You work for a shipping company that uses distribution centers with delivery lines to route packages. The company plans to install cameras on the lines to identify and monitor any visual damage to packages during transit.
Which solution should you select to automate the detection of damaged packages and alert for human inspection in real time while the packages are en route?
A. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
B. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.
C. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
D. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.
Answer: B
Explanation:
– Training an AutoML model on the corpus of images allows for the creation of a custom machine learning model specifically tailored to detect and track visual damage to packages. AutoML (Automated Machine Learning) simplifies the machine learning model creation process by automating tasks such as feature engineering, model selection, and hyperparameter tuning, making it easier and faster to build an accurate model.
– Building an API around the trained AutoML model enables seamless integration with the package tracking applications, allowing for real-time automated detection of damaged packages while they are in transit. This integration ensures that any flagged damaged packages can be promptly reviewed by human operators.
Why the other options are incorrect:
– Option A suggests using BigQuery machine learning to train the model at scale for analyzing packages in batches. While BigQuery ML can be useful for analyzing large datasets, it may not be the most suitable tool for real-time detection and tracking of damaged packages during transit.
– Option C recommends using the Cloud Vision API to detect damage and raise an alert through Cloud Functions. While Cloud Vision API is capable of detecting image content, it may not provide the level of customization and accuracy needed for detecting specific types of damage to packages.
– Option D proposes using TensorFlow to create a model trained on the corpus of images and analyzing for damaged packages using a Python notebook in Cloud Datalab. While TensorFlow is a powerful deep learning framework, it may require more manual effort and time for model creation and integration compared to using AutoML, which automates much of the machine learning process.
Question 7:
Scenario: You work for an advertising company and have developed a Spark ML model to predict click-through rates for advertisement blocks. Your company is migrating to Google Cloud from your on-premises data center, which is closing soon. The data will be migrated to BigQuery, including the data used for training your models. You need to quickly migrate your existing training pipelines to Google Cloud to continue periodic retraining of the Spark ML models.
What steps should you take to migrate your existing training pipelines to Google Cloud for retraining the Spark ML models periodically?
A.Use Vertex AI for training existing Spark ML models
B. Rewrite your models on TensorFlow, and start using Vertex AI
C. Use Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
Answer: C
Explanation:
1. Using Dataproc: Dataproc is a managed Spark and Hadoop service on Google Cloud that allows you to easily run existing Spark ML pipelines without significant changes. This option ensures a smooth transition for your existing training pipelines.
2. Reading data directly from BigQuery: Since your data will be migrated to BigQuery, it is efficient to read the data directly from BigQuery for training your Spark ML models. This approach eliminates the need to export data to other storage solutions, saving time and resources.
Now, let‘s explain why the other options are incorrect:
A. Using Vertex AI for training existing Spark ML models: Vertex AI is a machine learning platform that is more suitable for training models using TensorFlow and other Google Cloud-native tools. It is not the ideal solution for migrating and training existing Spark ML models.
B. Rewriting your models on TensorFlow and using Vertex AI: This option suggests a significant overhaul of your existing Spark ML models by rewriting them in TensorFlow, which might require a substantial amount of time and effort. It is not the most efficient solution for a rapid lift-and-shift migration.
D. Spinning up a Spark cluster on Compute Engine and training on data exported from BigQuery: This option involves unnecessary steps of exporting data from BigQuery to another storage solution before training the models. It adds complexity to the process and may not be the most efficient approach for your scenario.
Question 8:
Scenario: You work for a global shipping company and aim to train a model on 40 TB of data to predict potential delivery delays caused by ships in different regions. The model will consider various attributes gathered from diverse sources, including telemetry data in GeoJSON format updated hourly from each ship.
Which storage solution with native capabilities for prediction and geospatial processing should be selected for monitoring and identifying ships likely to cause delays within specific regions?
A.BigQuery
B. Cloud Bigtable
C. Cloud Datastore
D. Cloud SQL for PostgreSQL
Answer: A
Explanation:
BigQuery is a fully managed, serverless, and highly scalable data warehouse that is suitable for analyzing large datasets using SQL queries. It has native functionality for prediction and geospatial processing, making it a suitable choice for training a model on a large amount of data to predict which ships are likely to cause delivery delays in each geographic region. BigQuery can handle large amounts of data efficiently and is well-suited for analytical workloads involving multiple attributes collected from various sources.
Now, let‘s explain why the other options are incorrect:
B. Cloud Bigtable: Cloud Bigtable is a NoSQL database service designed for large analytical and operational workloads. While it is good for storing and retrieving large amounts of data quickly, it does not have native functionality for prediction or geospatial processing. It is more suitable for high-throughput, low-latency applications rather than predictive modeling with geospatial analysis.
C. Cloud Datastore: Cloud Datastore is a NoSQL document database for web and mobile applications. It is designed for storing and serving structured data for web applications, but it does not have native support for prediction or geospatial processing. It is more suitable for web application data storage rather than advanced analytics and machine learning tasks.
D. Cloud SQL for PostgreSQL: Cloud SQL is a fully managed relational database service that supports multiple database engines, including PostgreSQL. While Cloud SQL for PostgreSQL is suitable for transactional workloads and relational data storage, it may not be the best choice for storing and processing large amounts of data for predictive modeling and geospatial analysis. It is more geared towards traditional relational database use cases rather than big data analytics and machine learning tasks.
Question 9:
Scenario: In order to deploy additional dependencies to all nodes of a Cloud Dataproc cluster at startup, you need to use an existing initialization action. However, the company‘s security policies dictate that Cloud Dataproc nodes must not have Internet access for fetching resources.
What steps should be taken to address this issue?
A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
Answer: C
Explanation:
By copying all dependencies to a Cloud Storage bucket within your VPC security perimeter, you can ensure that the Cloud Dataproc nodes can access the required dependencies during startup without needing access to the Internet. This approach aligns with the company‘s security policies and allows the initialization action to fetch the resources from the Cloud Storage bucket within the VPC.
Why the other options are incorrect:
A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
This option is not suitable for the scenario described in the question. The requirement is to deploy additional dependencies to all nodes of the Cloud Dataproc cluster at startup, not specifically the Cloud SQL Proxy.
B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
This option contradicts the company‘s security policy, which prohibits Cloud Dataproc nodes from having access to the Internet. Using an SSH tunnel to provide Internet access would violate this policy.
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
This option is not relevant to the scenario described in the question. Granting the service account Network User role permissions does not address the need to deploy additional dependencies to the Cloud Dataproc cluster at startup without Internet access.
Question 10:
Scenario: You are managing a streaming Cloud Dataflow pipeline. Your team has developed a new version of the pipeline with a changed windowing algorithm and triggering strategy.
How can you update the current pipeline to the new version without losing any data?
A.Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
C. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
Answer: D
Explanation:
– When you stop a Cloud Dataflow job using the “Drain“ option, it allows the job to finish processing any data currently in the pipeline before stopping. This ensures that no data is lost during the update process.
– Creating a new Cloud Dataflow job with the updated code ensures that the new version of the pipeline with the different windowing algorithm and triggering strategy is used without affecting the existing running pipeline.
Why the other options are incorrect:
A. Updating the Cloud Dataflow pipeline in-flight with the –update option and setting the –jobName to the existing job name may cause data loss as the pipeline is updated without ensuring that all data is processed.
B. Updating the Cloud Dataflow pipeline in-flight with the –update option and setting the –jobName to a new unique job name may result in data loss as the existing job is not allowed to finish processing before the update.
C. Stopping the Cloud Dataflow pipeline with the Cancel option can lead to data loss as the job is abruptly terminated without allowing it to finish processing the data in the pipeline.
For a full set of 885 questions. Go to
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.