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Career Path
Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
NEW QUESTION # 237
One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?
- A. Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
- B. Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
- C. Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.
- D. Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.
Answer: B
Explanation:
To re-encrypt all of your CMEK-protected Cloud Storage data after a key has been exposed, and to ensure future writes are protected with a new key, creating a new Cloud KMS key and a new Cloud Storage bucket is the best approach. Here's why option C is the best choice:
* Re-encryption of Data:
* By creating a new Cloud Storage bucket and copying all objects from the old bucket to the new bucket while specifying the new Cloud KMS key, you ensure that all data is re-encrypted with the new key.
* This process effectively re-encrypts the data, removing any dependency on the compromised key.
* Ensuring CMEK Protection:
* Creating a new bucket and setting the new CMEK as the default ensures that all future objects written to the bucket are automatically protected with the new key.
* This reduces the risk of objects being written without CMEK protection.
* Deletion of Compromised Key:
* Once the data has been copied and re-encrypted, the old key can be safely deleted from Cloud KMS, eliminating the risk associated with the compromised key.
Steps to Implement:
* Create a New Cloud KMS Key:
* Create a new encryption key in Cloud KMS to replace the compromised key.
* Create a New Cloud Storage Bucket:
* Create a new Cloud Storage bucket and set the default CMEK to the new key.
* Copy and Re-encrypt Data:
* Use the gsutil tool to copy data from the old bucket to the new bucket while specifying the new CMEK key:
gsutil -o "GSUtil:gs_json_api_version=2" cp -r gs://old-bucket/* gs://new-bucket/
* Delete the Old Key:
* After ensuring all data is copied and re-encrypted, delete the compromised key from Cloud KMS.
Reference Links:
* Cloud KMS Documentation
* Cloud Storage Encryption
* Re-encrypting Data in Cloud Storage
NEW QUESTION # 238
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Execute to Google Cloud Storage; execute to Google Cloud Logging
- B. Execute to Google Cloud Storage; write to Google Cloud Logging
- C. Read and write to Google Cloud Storage; write to Google Cloud Logging
- D. Write to Google Cloud Storage; read to Google Cloud Logging
Answer: C
Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes
NEW QUESTION # 239
You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?
- A. Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.
- B. Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.
- C. Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.
- D. Adopt multi-regional Cloud Storage buckets in your architecture.
Answer: D
Explanation:
To ensure resilience and minimize the recovery point objective (RPO) with no impact on applications, using a dual-region bucket with turbo replication is the best approach. Here's why option D is the best choice:
* Dual-Region Buckets:
* Dual-region buckets store data redundantly across two distinct geographic regions, providing high availability and durability.
* This setup ensures that data remains available even if one region experiences a failure.
* Turbo Replication:
* Turbo replication ensures that data is replicated between the two regions within 15 minutes, aligning with the requirement to minimize the recovery point objective (RPO).
* This feature provides near real-time replication, significantly reducing the risk of data loss.
* No Impact on Applications:
* Applications continue to access the dual-region bucket without any changes, ensuring seamless operation even during a regional failure.
* The dual-region setup transparently handles failover, providing uninterrupted access to data.
Steps to Implement:
* Create a Dual-Region Bucket:
* Create a dual-region Cloud Storage bucket in the Google Cloud Console, selecting appropriate regions (e.g., us-central1 and us-east1).
* Enable Turbo Replication:
* Enable turbo replication to ensure rapid data replication between the selected regions.
* Configure Applications:
* Ensure that applications read and write to the dual-region bucket, benefiting from its high availability and durability.
* Test Failover:
* Simulate a regional failure to verify that the dual-region bucket and turbo replication meet the required RPO and ensure data resilience.
Reference Links:
* Google Cloud Storage Dual-Region
* Turbo Replication in Google Cloud Storage
NEW QUESTION # 240
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?
- A. multi-keyed data with very high latency
- B. single-keyed data with very high latency
- C. multi-keyed data with very low latency
- D. single-keyed data with very low latency
Answer: D
Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 241
Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?
- A. Configure Looker Studio to use a shorter data refresh interval to ensure fresh data is always displayed.
- B. Implement row-level security in BigQuery to restrict data access and reduce the amount of data processed by the queries.
- C. Create a materialized view in BigQuery that pre-calculates the aggregations and filters used in the Looker Studio dashboards.
- D. Use BigQuery BI Engine to accelerate query performance by caching frequently accessed data.
Answer: C,D
Explanation:
Pre-calculating commonly used aggregations and filters in a BigQuery materialized view significantly reduces the amount of computation required at query time, which directly improves dashboard responsiveness. Enabling BigQuery BI Engine further accelerates performance by keeping frequently accessed data in memory, optimizing interactive queries from Looker Studio and reducing latency for complex analytical workloads.
NEW QUESTION # 242
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?
- A. gcloud ml-engine jobs submit training local
- B. gcloud ml-engine local train
- C. You can't run a TensorFlow program on your own computer using Cloud ML Engine .
- D. gcloud ml-engine jobs submit training
Answer: B
Explanation:
Explanation
gcloud ml-engine local train - run a Cloud ML Engine training job locally This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.
This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.
Reference: https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train
NEW QUESTION # 243
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?
- A. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
- B. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore
- C. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore
- D. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
Answer: B
Explanation:
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last 30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low-latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.
NEW QUESTION # 244
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
- A. Redis
- B. MongoDB
- C. HDFS with Hive
- D. MySQL
- E. Cassandra
- F. HBase
Answer: B,C,F
Explanation:
Explanation:
NEW QUESTION # 245
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Convert the sharded tables into a single partitioned table
- B. Enable query caching so you can cache data from previous months
- C. Convert all daily log tables into date-partitioned tables
- D. Create separate views to cover each month, and query from these views
Answer: C
Explanation:
Explanation
NEW QUESTION # 246
You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?
- A. Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.
- B. Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.
- C. Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.
- D. Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.
Answer: A
Explanation:
A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired.
Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:
* Materialized views, ML models in data warehouse - Google Cloud
* Data Engineering with Google Cloud Platform - Packt Subscription
NEW QUESTION # 247
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
- A. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
- B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
- C. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
- D. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
Answer: A
NEW QUESTION # 248
Which software libraries are supported by Cloud Machine Learning Engine?
- A. Theano and Torch
- B. TensorFlow and Torch
- C. TensorFlow
- D. Theano and TensorFlow
Answer: C
Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions
about new data.
NEW QUESTION # 249
Which of these operations can you perform from the BigQuery Web UI?
- A. Upload multiple files using a wildcard.
- B. Upload a file in SQL format.
- C. Load data with nested and repeated fields.
- D. Upload a 20 MB file.
Answer: C
Explanation:
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.
Reference: https://cloud.google.com/bigquery/loading-data
NEW QUESTION # 250
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- A. Python using MapReduce
- B. Java using MapReduce
- C. PigLatin using Pig
- D. HiveQL using Hive
Answer: A
NEW QUESTION # 251
Why do you need to split a machine learning dataset into training data and test data?
- A. So you can use one dataset for a wide model and one for a deep model
- B. To make sure your model is generalized for more than just the training data
- C. To allow you to create unit tests in your code
- D. So you can try two different sets of features
Answer: B
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
NEW QUESTION # 252
You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?
- A. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps.
Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE. - B. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps.
At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data. - C. Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.
- D. Use a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
Answer: B
Explanation:
Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning.
Reference:
https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform
NEW QUESTION # 253
You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?
- A. Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset
- B. Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.
- C. Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.
- D. Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.
Answer: A
Explanation:
The BigQuery Data Viewer role allows users to read data and metadata from tables and views, but not to modify or delete them. By giving analysts this role on the shared dataset, you can ensure that they can access the data for analysis, but not change it. The BigQuery Data Editor role allows users to create, update, and delete tables and views, as well as read and write data. By giving analysts this role at the dataset level for their assigned dataset, you can provide them with individual workspaces where they can store their own tables and views, without affecting the shared dataset or other analysts' datasets. This way, you can achieve both data protection and data isolation for your team. Reference:
BigQuery IAM roles and permissions
Basic roles and permissions
NEW QUESTION # 254
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- A. Issue a command to restart the database servers.
- B. Retry the query every second until it comes back online to minimize staleness of data.
- C. Retry the query with exponential backoff, up to a cap of 15 minutes.
- D. Reduce the query frequency to once every hour until the database comes back online.
Answer: C
Explanation:
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff
NEW QUESTION # 255
Which of these operations can you perform from the BigQuery Web UI?
- A. Upload multiple files using a wildcard.
- B. Upload a file in SQL format.
- C. Load data with nested and repeated fields.
- D. Upload a 20 MB file.
Answer: C
Explanation:
Explanation
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.
Reference: https://cloud.google.com/bigquery/loading-data
NEW QUESTION # 256
You migrated a data backend for an application that serves 10 PB of historical product data for analytics.
Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?
- A. 1. Store the historical data in BigQuery for analytics.2. Use a materialized view to precompute the last state of a product.3. Serve the last state data directly from BigQuery to the API.
- B. 1. Store the historical data in BigQuery for analytics.2. In a Cloud SQL table, store the last state of the product after every product change.3. Serve the last state data directly from Cloud SQL to the API.
- C. 1. Store the historical data in Cloud SQL for analytics.2. In a separate table, store the last state of the product after every product change.3. Serve the last state data directly from Cloud SQL to the API.
- D. 1. Store the products as a collection in Firestore with each product having a set of historical changes.2.
Use simple and compound queries for analytics.3. Serve the last state data directly from Firestore to the API.
Answer: A
NEW QUESTION # 257
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
- B. Dataflow pipelines can consume data from other Google Cloud services
- C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
- D. Dataflow pipelines can be programmed in Java
Answer: C
Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 258
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create an embedding column
- B. Create a hash bucket
- C. Create a one-hot column
- D. Create a multi-hot column
Answer: A
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions.
This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html
NEW QUESTION # 259
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