Google Data Engineer Certification Dumps

Are you preparing for the Google Data Engineer Certification? Look no further! Our comprehensive guide on Google Data Engineer Certification Dumps will help you ace the exam. Whether you’re searching for Professional Data Engineer Dumps, Google Cloud Data Engineer Dumps, or GCP Data Engineer Dumps, we’ve got you covered.

1. Google Data Engineer Certification

The Google Data Engineer Certification is a professional certificate for individuals who looking to validate their expertise in data engineering within the Google Cloud ecosystem.
This certification assesses one’s ability to design, build, and operationalize data processing systems, focusing on GCP tools like BigQuery, Dataflow, and Pub/Sub.

2. Why the Google Cloud Data Engineer Certification Matters

The certification demonstrates your ability to:

• Design scalable data processing systems.
• Implement machine learning models with BigQuery and TensorFlow.
• Optimize data storage and pipelines using GCP services like Dataflow, Bigtable, and Pub/Sub.

Employers prioritize certified engineers for roles in data architecture, analytics, and cloud engineering. However, earning this credential requires genuine skill, not memorizing answers from questionable sources.

3. Study Resources for the Google Data Engineer Exam

1. Official Google Cloud Training
   • Enroll in Google’s Data Engineer Learning Path, which includes labs, courses, and hands-on practice.
   • Review the exam guide to understand tested domains.
2. Hands-On Practice with Qwiklabs
   Gain real-world experience by completing GCP labs on data pipelines, BigQuery, and machine learning.
3. Community-Driven Study Groups
   Join forums like Reddit’s r/googlecloud or LinkedIn groups to discuss concepts with peers.
4. Practice Exams from Trusted Providers
   Use official practice tests from Google Cloud or reputable platforms like Coursera to assess readiness.

4. Pro Tips to Ace the Exam

1. Focus on Core Services
   Master BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage. Understand how they integrate in end-to-end solutions.
2. Learn Cost Optimization Techniques
   Google heavily emphasizes cost management. Study pricing models, resource scaling, and monitoring with Stackdriver.
3. Simulate Real Scenarios
   Build projects like real-time analytics dashboards or batch processing pipelines to reinforce skills.
4. Time Management
   The exam includes 50 questions in 2 hours. Practice under timed conditions to improve efficiency.

5. Career Advantages

The Google Data Engineer Certification can ease the way for job recommendations like Data Engineer and Cloud Architect, where many organizations seek professionals with verified Google Cloud Platform expertise.
Such a professional credential would speak volumes indeed in a crowded job market, evidencing advanced data engineering potential.

For more information, visit the Google Cloud Data Engineer Certification page.

Google Data Engineer Certification Dumps

Q1. You currently have a single on-premises Kafka cluster in a data center in the US-East region that is responsible for ingesting messages from IoT devices globally. Because large parts of the globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud-native architecture for this scenario?

1. Edge TPUs as sensor devices for storing and transmitting messages.
2. Cloud Dataflow is connected to the Kafka cluster to scale the processing of incoming messages.
3. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.✔️
4. A Kafka cluster virtualized on Compute Engine in the US-East with Cloud Load Balancing to connect to devices worldwide.

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Q2. You decided to use Cloud Datastore to ingest vehicle telemetry data in real-time. You want to build a storage system that will account for long-term data growth while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this?

1. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.✔️
2. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
3. Use managed export, and then import the data into a BigQuery table created just for that export, and delete the temporary export files.✔️
4. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
5. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

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Q3. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the initial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt?

1. Denormalize the data as much as possible.✔️
2. Preserve the structure of the data as much as possible.
3. Use BigQuery UPDATE to further reduce the size of the dataset.
4. Develop a data pipeline where status updates are appended to BigQuery instead of being updated.
5. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery’s support for external data sources to query.✔️

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Q4. You are designing a cloud-native historical data processing system to meet the following conditions:

• The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools, including Cloud Dataproc, BigQuery, and Compute Engine.
• A streaming data pipeline stores new data daily.
• Performance is not a factor in the solution.
• The solution design should maximize availability.

How should you design data storage for this solution?

1. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and perform analysis as needed.
2. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
3. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
4. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.✔️

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Q5. You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

1. Store and process the entire dataset in BigQuery.
2. Store and process the entire dataset in Cloud Bigtable.
3. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.✔️
4. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

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Q6. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You’ve collected a labeled dataset that has, on average, 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-of-Concept) within a few working days. What should you do?

1. Use Cloud Vision AutoML with the existing dataset.✔️
2. Use Cloud Vision AutoML, but reduce your dataset twice.
3. Use Cloud Vision API by providing custom labels as recognition hints.
4. Train your image recognition model leveraging transfer learning techniques.

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Q7. You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops that your team has implemented. These ops are used inside your main training loop and perform bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

1. Use Cloud TPUs without any additional adjustments to your code.
2. Use Cloud TPUs after implementing GPU kernel support for your custom ops.
3. Use Cloud GPUs after implementing GPU kernel support for your custom ops.✔️
4. Stay on CPUs, and increase the size of the cluster you’re training your model on.

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Q8. You work on a regression problem in a natural language processing domain, and you have 100M labeled examples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discovered that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

1. Increase the share of the test sample in the train-test split.
2. Try to collect more data and increase the size of your dataset.
3. Try out regularization techniques (e.g., dropout or batch normalization) to avoid overfitting.
4. Increase the complexity of your model by, e.g., introducing an additional layer or increasing the size of vocabularies or ngrams used.✔️

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Q9. 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?

1. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
2. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.✔️
3. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
4. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time before the corruption.

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Q10. The marketing team at your organization provides regular updates on a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

1. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
2. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
3. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
4. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.✔️

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Q11. As your organization expands its usage of GCP, many teams have started to create their projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take?

1. Use Cloud Deployment Manager to automate access provision.
2. Introduce resource hierarchy to leverage access control policy inheritance.✔️
3. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
4. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.✔️
5. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

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Q12. Your United States-based company has created an application for assessing and responding to user actions. The primary table’s data volume grows by 250,000 records per second. Many third parties use your application’s APIs to build the functionality into their front-end applications. Your application’s APIs should comply with the following requirements:

• Single global endpoint
• ANSI SQL support
• Consistent access to the most up-to-date data

What should you do?

1. Implement BigQuery with no region selected for storage or processing.
2. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.✔️
3. Implement Cloud SQL for PostgreSQL with the master in North America and read replicas in Asia and Europe.
4. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

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Q13. A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with a latency under 100 milliseconds. You use the following query to generate predictions:

SELECT predicted_label, user_id
FROM ML.PREDICT (MODEL 'dataset.model', table user_features)

How should you create the ML pipeline?

1. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
2. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
3. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
4. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.✔️

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Q14. You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time.

Consumers will receive the data in the following ways:

• Real-time event stream
• ANSI SQL access to real-time stream and historical data
• Batch historical exports

Which solution should you use?

1. Cloud Dataflow, Cloud SQL, Cloud Spanner
2. Cloud Pub/Sub, Cloud Storage, BigQuery✔️
3. Cloud Dataproc, Cloud Dataflow, BigQuery
4. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

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Q15. You are building a new application that needs to collect data in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:

• Decoupling the producer from the consumer
• Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
• Near real-time SQL query
• Maintain at least 2 years of historical data, which will be queried with SQL

Which pipeline should you use to meet these requirements?

1. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
2. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
3. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
4. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.✔️

Q16. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in Europe west4 and has a maximum of 3 workers, for instance, type n-standard. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase the performance of your pipeline?

1. Increase the number of max workers✔️
2. Use a larger instance type for your Cloud Dataflow workers✔️
3. Change the zone of your Cloud Dataflow pipeline to run in us-central1
4. Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
5. Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

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Q17. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time-series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take?

1. Configure your Cloud Dataflow pipeline to use local execution
2. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions✔️
3. Increase the number of nodes in the Cloud Bigtable cluster✔️
4. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
5. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

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Q18. 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?

1. Create a Cloud Dataproc Workflow Template✔️
2. Create an initialization action to execute the jobs
3. Create a Directed Acyclic Graph in Cloud Composer
4. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

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Q19. You are building a new data pipeline to share data between two different types of applications: job generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

1. Create an API using App Engine to receive and send messages to the applications
2. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them✔️
3. Create a table on Cloud SQL, and insert and delete rows with the job information
4. Create a table on Cloud Spanner, and insert and delete rows with the job information

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Q20. You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

1. The current epoch time
2. A concatenation of the product name and the current epoch time
3. A random universally unique identifier number (version 4 UUID)✔️
4. The original order identification number from the sales system, which is a monotonically increasing integer

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Q21. Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

1. Enable data access logs in each Data Analyst’s project. Restrict access to Stackdriver Logging via Cloud IAM roles.
2. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts’ projects. Restrict access to the Cloud Storage bucket.
3. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project with the exported logs.
4. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.✔️

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Q22. Each analytics team in your organization is running BigQuery jobs in their projects. You want to enable each team to monitor slot usage within their projects. What should you do?

1. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
2. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project✔️
3. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the total slots, and create a Stackdriver Monitoring dashboard based on the custom metric
4. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the total slots, and create a Stackdriver Monitoring dashboard based on the custom metric

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Q23. You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

1. Update the Cloud Dataflow pipeline in-flight, bypassing the -update option with the –jobName set to the existing job name✔️
2. Update the Cloud Dataflow pipeline in-flight, bypassing the –update option with the –jobName set to a new unique job name
3. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
4. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

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Q24. You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

1. Use Transfer Appliance to copy the data to Cloud Storage✔️
2. Use gsutil cp “J to compress the content being uploaded to Cloud Storage
3. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
4. Use trickle or ionic 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

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Q25. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month, the schema of the files changes. Your requirements for implementing these transformations include:

• Executing the transformations on a schedule
• Enabling non-developer analysts to modify transformations
• Providing a graphical tool for designing transformations

What should you do?

1. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis✔️
2. Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables with an SQL query
3. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes
4. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then, implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading it into BigQuery

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Q26. You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hiv- E is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster’s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc?

1. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.
2. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.
3. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them to HDFS. Mount the Hive tables from HDFS.
4. Leverage the Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.✔️
5. Load the ORC files into BigQuery. Leverage the BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.✔️

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Q27. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

1. Cloud Scheduler
2. Cloud Dataflow
3. Cloud Functions
4. Cloud Composer✔️

Q28. You work for a shipping company that has distribution centers where packages move on delivery lines to be routed properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

1. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
2. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.✔️
3. Use the Cloud Vision API to detect damage and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
4. 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 damaged packages.

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Q29. You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

1. Assign the users/groups data viewer access at the table level for each table
2. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
3. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views✔️
4. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups’ data viewer access to the datasets in which the authorized views reside

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Q30. You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from computing, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100 GB RAM). Analysis shows that this particular Hadoop jo- B is disk I/O intensive. You want to resolve the issue. What should you do?

1. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
2. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS✔️
3. Allocate more CPU cores to the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
4. Allocate an additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

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Q31. You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you’ve been using will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

1. Use Cloud ML Engine for training existing Spark ML models
2. Rewrite your models on TensorFlow, and start using Cloud ML Engine
3. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery✔️
4. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

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Q32. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

1. BigQuery✔️
2. Cloud Bigtable
3. Cloud Datastore
4. Cloud SQL for PostgreSQL

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Q33. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

1. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.✔️
2. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
3. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.
4. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

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Q34. You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

1. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
2. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.✔️
3. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
4. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

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Q35. Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:

• The ability to seek a particular offset in a topic, possibly back to the start of all data ever captured
• Support for publish/subscribe semantics on hundreds of topics
• Retain per-key ordering

Which system should you choose?

1. Apache Kafka✔️
2. Cloud Storage
3. Cloud Pub/Sub
4. Firebase Cloud Messaging

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Q36. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

1. Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://✔️
2. Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
3. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://
4. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

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Q37. Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

1. Perform hyperparameter tuning
2. Train a classifier with deep neural networks, because neural networks would always beat SVMs
3. Deploy the model and measure the real-world AUC; it’s always higher because of generalization
4. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) to get the highest AUC✔️

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Q38. You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet, so public initialization actions cannot fetch resources. What should you do?

1. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
2. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
3. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter✔️
4. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

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Q39. You need to choose a database for a new project that has the following requirements:

• Fully managed
• Able to automatically scale up
• Transactionally consistent
• Able to scale up to 6 TB
• Able to be queried using SQL

Which database do you choose?

1. Cloud SQL✔️
2. Cloud Bigtable
3. Cloud Spanner
4. Cloud Datastore

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Q40. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

1. Cloud SQL✔️
2. Cloud Bigtable
3. Cloud Spanner
4. Cloud Datastore

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Q41. You need to choose a database to store time-series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for the future growth of the dataset. Which database and data model should you choose?

1. Create a table in BigQuery, and append the new samples for CPU and memory to the table
2. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
3. Create a narrow table in Cloud Bigtable with a row key that combines the Compute Engine computer identifier with the sample time at each second✔️
4. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combines the values for each second as column data.

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Q42. You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the “Trust No One” (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

1. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
2. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once.
3. Specify the customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
4. Specify the customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.✔️

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Q43. You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products/features of the platform. What should you do?

1. Export the information to Cloud Stackdriver, and set up an Alerting policy✔️
2. Run a Virtual Machine in the Compute Engine with Airflow, and export the information to Stackdriver
3. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
4. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Q44. You are building storage for files for a data pipeline on Google Cloud. You want to support JSON files. The schema of these files will occasionally change. Your analyst teams will use running aggregate ANSI SQL queries on this data. What should you do?

1. Use BigQuery for storage. Provide format files for data load. Update the format files as needed.
2. Use BigQuery for storage. Select “Automatically detect” in the Schema section.✔️
3. Use Cloud Storage for storage. Link data as temporary tables in BigQuery and turn on the “Automatically detect” option in the Schema section of BigQuery.
4. Use Cloud Storage for storage. Link data as permanent tables in BigQuery and turn on the “Automatically detect” option in the Schema section of BigQuery.

Q45. Your company is loading CSV files into BigQuery. The data is fully imported successfully; however, the imported data does not match byte-for-byte to the source file. What is the most likely cause of this problem?

1. The CSV data loaded in BigQuery is not flagged as CSV.
2. The CSV data had invalid rows that were skipped on import.
3. The CSV data loaded in BigQuery does not use BigQuery’s default encoding.✔️
4. The CSV data has not gone through an ETL phase before loading into BigQuery.

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