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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
Achieving the Google Professional-Data-Engineer certification is a significant accomplishment and can help professionals advance their careers in the field of data engineering. Google Certified Professional Data Engineer Exam certification demonstrates to employers and clients that the candidate has the skills and knowledge necessary to design and manage complex data processing systems using the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification also provides access to a community of certified professionals and other resources that can help candidates stay up-to-date with the latest trends and best practices in data engineering.
NEW QUESTION # 157
Your company is planning to migrate a large on-premises data warehouse to BigQuery. The data is currently stored in a proprietary, vendor-specific format. You need to perform a batch migration of this data to BigQuery. What should you do?
- A. Use the BigQuery Data Transfer Service.
- B. Use Datastream to replicate the data in real time.
- C. Export the data to CSV files, upload the files to Cloud Storage, then load the files into BigQuery.
- D. Use the bq command-line tool to load the data directly from the on-premises data warehouse.
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
The challenge here is dealing with a "proprietary, vendor-specific format" for a one-time batch migration.
Option C is the correct answer because it represents the most universal and reliable pattern for migration from any source system. By first exporting the data into a standard, interoperable format like CSV (or preferably, a self-describing format like Avro or Parquet), you decouple the process from the proprietary source. These standard files can then be easily uploaded to Cloud Storage (the recommended staging area for BigQuery loads) and loaded into BigQuery in a highly performant and parallelized manner.
Option A is incorrect because the bq command-line tool cannot connect directly to an on-premises data warehouse to pull data. It loads data from files or streams.
Option B is incorrect because the BigQuery Data Transfer Service (DTS) has connectors for specific, common data sources (like Teradata, Redshift, S3). It is unlikely to have a connector for a generic
"proprietary, vendor-specific format."
Option D is incorrect because Datastream is a Change Data Capture (CDC) service designed for real-time replication of databases, not for a large-scale, one-time batch migration of a data warehouse.
Reference (Google Cloud Documentation Concepts):Google Cloud's "Data warehouse migration to BigQuery" guide outlines several migration strategies. For batch data transfer, the recommended path is Extract, Transfer, Load (ETL). This involves extracting data from the source into files (in formats like CSV, Avro, Parquet), transferring those files to Cloud Storage, and then loading them into BigQuery. This approach is recommended for its reliability and compatibility with any source system
NEW QUESTION # 158
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?
- A. Put the data into Google Cloud Storage.
- B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
- C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
- D. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/dataproc/
NEW QUESTION # 159
Which of the following is not possible using primitive roles?
- A. Give a user access to view all datasets in a project, but not run queries on them.
- B. Give GroupA owner access and GroupB editor access for all datasets in a project.
- C. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
- D. Give UserA owner access and UserB editor access for all datasets in a project.
Answer: A
Explanation:
Explanation
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles
NEW QUESTION # 160
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 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?
- A. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
- B. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.
- C. Edge TPUs as sensor devices for storing and transmitting the messages.
- D. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
Answer: D
NEW QUESTION # 161
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding What should you do?
- A. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
- B. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.
- C. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
- D. Create a Spark job and submit it to Dataproc Serverless.
Answer: A
Explanation:
Cloud Data Fusion is a fully managed, cloud-native data integration service that allows you to build and manage data pipelines with a graphical interface. Wrangler is a feature of Cloud Data Fusion that enables you to interactively explore, clean, and transform data using a spreadsheet-like UI. You can use Wrangler to normalize the data in BigQuery by applying various directives, such as parsing, formatting, replacing, and validating data. You can also preview the results and export the wrangled data to BigQuery or other destinations. You can then set up a recurring job in Cloud Data Fusion to run the Wrangler pipeline on a schedule, such as weekly or daily. This way, you can create a quick and code-free solution to normalize the data for your reports. References:
* Cloud Data Fusion overview
* Wrangler overview
* Wrangle data from BigQuery
* [Scheduling pipelines]
NEW QUESTION # 162
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?
- A. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
- B. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.
- C. Update the job to increase the maximum number of workers.
- D. Enable Vertical Autoscaling to let the pipeline use larger workers.
Answer: A
Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage. This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. Reference:
1: Streaming pipelines
2: Batch vs Streaming Performance in Google Cloud Dataflow
3: Deploy Dataflow pipelines
4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
5: Managing costs for Dataflow batch and streaming data processing
NEW QUESTION # 163
You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?
- A. Leave the Authorized Network empty. Use Cloud SQL Auth proxy on all applications.
- B. Add all application networks to Authorized Network and regularly update them.
- C. Add CIDR 0.0.0.0/0 network to Authorized Network. Use Cloud SOL Auth proxy on all applications.
- D. Add CIDR 0.0.0.0/0 network to Authorized Network. Use Identity and Access Management (1AM) to add users.
Answer: A
Explanation:
To securely connect multiple applications with dynamic public IP addresses to a Cloud SQL instance using public IP, the Cloud SQL Auth proxy is the best solution. This proxy provides secure, authorized connections to Cloud SQL instances without the need to configure authorized networks or deal with IP whitelisting complexities.
Cloud SQL Auth Proxy:
The Cloud SQL Auth proxy provides secure, encrypted connections to Cloud SQL.
It uses IAM permissions and SSL to authenticate and encrypt the connection, ensuring data security in transit.
By using the proxy, you avoid the need to constantly update authorized networks as the proxy handles dynamic IP addresses seamlessly.
Authorized Network Configuration:
Leaving the authorized network empty means no IP addresses are explicitly whitelisted, relying solely on the Auth proxy for secure connections.
This approach simplifies network management and enhances security by not exposing the Cloud SQL instance to public IP ranges.
Dynamic IP Handling:
Applications with dynamic IP addresses can securely connect through the proxy without the need to modify authorized networks.
The proxy authenticates connections using IAM, making it ideal for environments where application IPs change frequently.
Google Data Engineer Reference:
Using Cloud SQL Auth Proxy
Cloud SQL Security Overview
Setting up the Cloud SQL Auth Proxy
By using the Cloud SQL Auth proxy, you ensure secure, authorized connections for applications with dynamic public IPs without the need for complex network configurations.
NEW QUESTION # 164
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
- A. SSH tunnel
- B. VPN connection
- C. FTP connection
- D. Special browser
Answer: A
Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces
NEW QUESTION # 165
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
- A. sparse_column_with_keys
- B. categorical_column_with_unknown_values
- C. categorical_column_with_vocabulary_list
- D. categorical_column_with_hash_bucket
Answer: D
Explanation:
Explanation
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide
NEW QUESTION # 166
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?
- A. Cloud SQL
- B. Cloud Datastore
- C. Cloud Spanner
- D. Cloud Bigtable
Answer: C
Explanation:
https://cloud.google.com/products/databases
NEW QUESTION # 167
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?
- A. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period.Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.
- B. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
- C. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.
- D. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
Answer: A
Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete.
This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. References:
* Signed URLs | Cloud Storage Documentation
* V4 signing process with Cloud Storage tools
* V4 signing process with your own program
* Using a URL list file
* What Is a 403 Forbidden Error (and How Can I Fix It)?
NEW QUESTION # 168
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?
- A. Write a Spark-based stored procedure.
- B. Build BigQuery user-defined functions (UDFs).
- C. Create Dataplex data quality tasks.
- D. Build Dataform assertions into your code
Answer: D
Explanation:
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query. If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases. Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. Reference: Test tables with assertions | Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform
NEW QUESTION # 169
Your team has created several BigQuery curated datasets containing anonymized industry benchmark data.
You want to make these datasets easily discoverable and accessible for querying by external partner companies within their own Google Cloud projects. You need a secure and scalable solution. What should you do?
- A. Publish the datasets as listings within BigQuery sharing (Analytics Hub).
- B. Export the datasets to partner-specific Cloud Storage buckets.
- C. Create authorized views for each dataset and grant access to each partner.
- D. Grant the roles/bigquery.dataViewer IAM role to the partner group email addresses on the datasets.
Answer: A
Explanation:
Analytics Hub (part of BigQuery sharing) is the enterprise-grade, Google-recommended way to share data across organizational boundaries.
* Discoverability: Analytics Hub provides a central portal where partners can search for and "subscribe" to datasets. Traditional IAM grants (A) do not provide a discovery interface.
* Scalability: You can publish a single "listing" and allow many partners to subscribe to it. Each subscriber gets a "linked dataset" in their project.
* Security & Governance: The publisher retains control. You can see who has subscribed and even enforce "data egress" controls to prevent partners from copying or exporting the data while still allowing them to query it.
* Correcting other options:
* A: This works for a few users but lacks the metadata, searchability, and formal "subscription" lifecycle of Analytics Hub.
* C: Authorized views are for internal security within a project/org and don't solve the cross-org discovery problem.
* D: Exporting data creates data silos, increases storage costs, and loses the real-time query benefits of BigQuery.
Reference: Google Cloud Documentation on Analytics Hub:
"Analytics Hub is a data exchange platform that lets you share data and insights at scale across organizational boundaries... Listings let you share data without replicating the shared data... Subscribers discover data through the exchange and create a linked dataset in their own project to query the data." (Source: Introduction to BigQuery sharing)
NEW QUESTION # 170
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?
- A. Train your own image recognition model leveraging transfer learning techniques.
- B. Use Cloud Vision AutoML with the existing dataset.
- C. Use Cloud Vision API by providing custom labels as recognition hints.
- D. Use Cloud Vision AutoML, but reduce your dataset twice.
Answer: B
NEW QUESTION # 171
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 compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary dat
a. 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 job is disk I/O intensive. You want to resolve the issue. What should you do?
- A. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
- B. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
- C. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
- D. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
Answer: A
NEW QUESTION # 172
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?
- A. 1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product. - B. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.
3. Assign permissions to the data engineering teams at the zone level. - C. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.
2 Provide each data engineering team access to the virtual lake. - D. 1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
Answer: D
Explanation:
This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12. By creating a Dataplex virtual lake for each data product, you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3. By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.
Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2. Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34. Option C is better than option A and B, but it does not create multiple zones for landing, raw, and curated data, which may compromise the security and quality of the data products5. Reference:
1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog
2: Data Mesh - 7 Effective Practices to Get Started - Confluent
3: Best practices | Dataplex | Google Cloud
4: Secure your lake | Dataplex | Google Cloud
5: Zones | Dataplex | Google Cloud
[6]: Managing a Data Mesh with Dataplex - ROI Training
NEW QUESTION # 173
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase the size of the Google Persistent Disk on your server.
- B. Increase your network bandwidth from Compute Engine to Cloud Storage.
- C. Increase your network bandwidth from your datacenter to GCP.
- D. Increase the CPU size on your server.
Answer: C
NEW QUESTION # 174
You are updating the code for a subscriber to a Put/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. You subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?
- A. Set up the Pub/Sub emulator on your local machine Validate the behavior of your new subscriber togs before deploying it to production
- B. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created
- C. Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successful acknowledged if an error occurs after deployment, re-deliver any messages captured by the dead-letter queue
- D. Use Cloud Build for your deployment if an error occurs after deployment, use a Seek operation to locate a tmestamp logged by Cloud Build at the start of the deployment
Answer: B
NEW QUESTION # 175
A web server sends click events to a Pub/Sub topic as messages. The web server includes an event Timestamp attribute in the messages, which is the time when the click occurred. You have a Dataflow streaming job that reads from this Pub/Sub topic through a subscription, applies some transformations, and writes the result to another Pub/Sub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their event Timestamp and publish Time. What is the problem and what should you do?
- A. Messages in your Dataflow job are processed in less than 30 seconds, but your job cannot keep up with the backlog in the Pub/Sub subscription. Optimize your job or increase the number of workers to fix this.
- B. The web server is not pushing messages fast enough to Pub/Sub. Work with the web server team to fix this.
- C. The advertising department is causing delays when consuming the messages. Work with the advertising department to fix this.
- D. Messages in your Dataflow job are taking more than 30 seconds to process. Optimize your job or increase the number of workers to fix this.
Answer: A
Explanation:
To ensure that the advertising department receives messages within 30 seconds of the click occurrence, and given the current system lag and data freshness metrics, the issue likely lies in the processing capacity of the Dataflow job. Here's why option B is the best choice:
* System Lag and Data Freshness:
* The system lag of 5 seconds indicates that Dataflow itself is processing messages relatively quickly.
* However, the data freshness of 40 seconds suggests a significant delay before processing begins, indicating a backlog.
* Backlog in Pub/Sub Subscription:
* A backlog occurs when the rate of incoming messages exceeds the rate at which the Dataflow job can process them, causing delays.
* Optimizing the Dataflow Job:
* To handle the incoming message rate, the Dataflow job needs to be optimized or scaled up by increasing the number of workers, ensuring it can keep up with the message inflow.
Steps to Implement:
* Analyze the Dataflow Job:
* Inspect the Dataflow job metrics to identify bottlenecks and inefficiencies.
* Optimize Processing Logic:
* Optimize the transformations and operations within the Dataflow pipeline to improve processing efficiency.
* Increase Number of Workers:
* Scale the Dataflow job by increasing the number of workers to handle the higher load, reducing the backlog.
Reference Links:
* Dataflow Monitoring
* Scaling Dataflow Jobs
NEW QUESTION # 176
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?
- A. Enable the Object Versioning feature. Create a copy in a bucket in a different region.
- B. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
- C. Set a retention policy. Lock the retention policy.
- D. Enable the Object Versioning feature. Add a lifecycle rule.
Answer: C
Explanation:
To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified.
This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference Links:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy
NEW QUESTION # 177
You are implementing security best practices on your data pipeline. Currently, you are manually executing
jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-
public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud
Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?
- A. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files
and write to BigQuery - B. Restrict the Google Cloud Storage bucket so only you can see the files
- C. Use a service account with the ability to read the batch files and to write to BigQuery
- D. Grant the Project Owner role to a service account, and run the job with it
Answer: D
NEW QUESTION # 178
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In order to be eligible for the exam, candidates must have at least three years of industry experience, with at least one year of experience working with Google Cloud Platform. Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions and is designed to be completed within two hours. The passing score for the exam is 70%.
Penetration testers simulate Professional-Data-Engineer exam: https://www.testpassking.com/Professional-Data-Engineer-exam-testking-pass.html
Free Test Engine For Google Certified Professional Data Engineer Exam Certification Exams: https://drive.google.com/open?id=1ZUWJAz0qNZS1XJt6mVMZUs00KP6qaVIb