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Google Professional-Data-Engineer certification is a valuable credential for data professionals who work with Google Cloud Platform. It demonstrates their expertise in designing and building data processing systems, as well as their ability to leverage the power of Google Cloud Platform to solve complex business problems. With the right preparation and dedication, candidates can achieve this certification and enhance their careers in the rapidly growing field of big data.
What is the duration, language, and format of Google Professional Data Engineer Exam
- Format: Multiple choices, multiple answers
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Passing score: 80%
NEW QUESTION # 178
The marketing team at your organization provides regular updates of 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?
- A. 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.
- B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
- C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
- D. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
Answer: A
NEW QUESTION # 179
You are building a new data pipeline to share data between two different types of applications: jobs 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?
- A. Create an API using App Engine to receive and send messages to the applications
- B. Create a table on Cloud SQL, and insert and delete rows with the job information
- C. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- D. Create a table on Cloud Spanner, and insert and delete rows with the job information
Answer: A
NEW QUESTION # 180
Scaling a Cloud Dataproc cluster typically involves ____.
- A. deleting applications from unused nodes periodically
- B. increasing or decreasing the number of master nodes
- C. moving memory to run more applications on a single node
- D. increasing or decreasing the number of worker nodes
Answer: D
Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1 ) increase the number of workers to make a job run faster
2 ) decrease the number of workers to save money
3 ) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage Reference: https://cloud.google.com/dataproc/docs/concepts/scaling-clusters
NEW QUESTION # 181
You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using SideInputs to join data. You noticed that the pipeline is taking longer to complete than expected; what should you do to expedite the Dataflow job?
- A. Reduce the batch size
- B. Use CoGroupByKey instead of the Sidelnput
- C. Retry records that throw an error
- D. Switch to compressed Avro files
Answer: B
Explanation:
There are a lot of reference doc to tell about comparison between them
Reference:
https://cloud.google.com/architecture/building-production-ready-data-pipelines-using-dataflow-developing-and-testing#choose_correctly_between_side_inputs_or_cogroupbykey_for_joins
https://cloud.google.com/blog/products/data-analytics/guide-to-common-cloud-dataflow-use-case-patterns-part-2
https://stackoverflow.com/questions/58080383/sideinput-i-o-kills-performance
NEW QUESTION # 182
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- A. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- B. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
- C. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
- D. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
Answer: A
Explanation:
Explanation
NEW QUESTION # 183
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data.
You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?
- A. Use Analytics Hub to facilitate data sharing.
- B. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery dataViewer role on the dataset.
- C. Create authorized datasets to publish shared data in the subscribing team's project.
- D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.
Answer: A
Explanation:
Google's Analytics Hub is specifically designed to enable secure, efficient, and self-service data sharing within an organization or across organizations. It provides a centralized platform for:
1. Publishing data: Teams can publish datasets as data listings in the hub.
2. Discovery: Other teams can browse the hub and subscribe to datasets of interest.
3. Access control: Data access is managed securely, ensuring that only authorized users or teams can view and utilize the data.
4. Freshness: Data is not duplicated or copied; instead, subscribers query the source dataset directly, ensuring data freshness.
5. Cost efficiency: By avoiding unnecessary data duplication (e.g., creating copies of datasets), you minimize storage costs and maximize performance.
NEW QUESTION # 184
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
- A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
- B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
- C. Use K-means Clustering to detect faces in the pixels.
- D. Use feature engineering to add features for eyes, noses, and mouths to the input data.
Answer: B
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
NEW QUESTION # 185
Which of the following are feature engineering techniques? (Select 2 answers)
- A. Feature prioritization
- B. Bucketization of a continuous feature
- C. Crossed feature columns
- D. Hidden feature layers
Answer: B,C
Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into. Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model
NEW QUESTION # 186
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?
- A. Log debug information in each ParDo function, and analyze the logs at execution time.
- B. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
- C. Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks
- D. Insert output sinks after each key processing step, and observe the writing throughput of each block.
Answer: B
Explanation:
When Dataflow fuses multiple transformations into a single stage (step), it can make it harder to pinpoint which specific part of that fused stage is causing a bottleneck because internal metrics for individual ParDos within the fused stage might not be as distinct.
* Reshuffle Operation (Option D):Inserting a Reshuffle (or GroupByKey followed by ungrouping, which forces a shuffle) operation between logical processing steps in your Beam pipeline prevents Dataflow from fusing those steps. A shuffle operation acts as a barrier to fusion. This materializes the intermediate PCollection and forces data to be redistributed across workers.
* Benefit for Debugging:By breaking the fusion, the Dataflow monitoring UI will display distinct steps for the operations before and after the Reshuffle. This allows you to observe metrics like processing time, throughput, and watermarks for each now-separated step, making it much easier to identify which part of your original fused logic is the bottleneck.
Let's analyze why other options are less effective for this specific problem of afused step:
* A (Verify service account permissions):While important for overall pipeline health, permission issues usually result in outright failures or errors in logs, not typically a slowdown within a successfully running (albeit slow) fused step.
* B (Insert output sinks):Adding actual output sinks (like writing to Pub/Sub or GCS) after each key step would also break fusion and allow you to measure throughput. However, it's a more heavyweight approach than Reshuffle. It introduces I/O overhead and requires setting up and managing these temporary sinks. Reshuffle is a lighter-weight way to achieve the same goal of breaking fusion for diagnostic purposes within the pipeline itself.
* C (Log debug information):Logging can be helpful, but if the entire fused step is slow, logs might not easily distinguish which internal operation is the culprit without very careful and verbose logging.
Analyzing potentially massive volumes of logs for performance bottlenecks can be less direct than observing stage metrics in the Dataflow UI once fusion is broken.
Using Reshuffle is a standard technique recommended by Google Cloud for debugging performance issues in fused Dataflow stages.
Reference:
Google Cloud Documentation: Dataflow > Troubleshooting Dataflow pipelines > Common Dataflow errors and troubleshooting steps > Pipeline is slow or stuck. "Break transform fusion: Certain transforms in your pipeline might be fused together into a single stage for optimization. If a particular fused stage is causing a bottleneck, you can temporarily add Reshuffle transforms between the fused transforms to break them into smaller, separate stages. This allows you to get more visibility into the performance of each individual transform and isolate the bottleneck." Apache Beam Documentation: Programming Guide > Pipeline I/O > Reshuffle."Reshuffle can be used to prevent fusion, and ensure that data is materialized and redistributed." (While the primary purpose of Reshuffle is often related to data distribution and freshness, a side effect and common use case is to break fusion for monitoring and debugging).
NEW QUESTION # 187
Which Java SDK class can you use to run your Dataflow programs locally?
- A. LocalRunner
- B. DirectPipelineRunner
- C. LocalPipelineRunner
- D. MachineRunner
Answer: B
Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners
/DirectPipelineRunner
NEW QUESTION # 188
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transaction_header and sales_transaction_line, have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried.
You need to model the sales_transaction_header and sales_transaction_line tables to improve the performance of data analytics queries. What should you do?
- A. Create a sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
- B. Create separate sales_transaction_header and sales_transaction_line tables and, when querying, specify the sales_transaction_line first in the WHERE clause.
- C. Create a sales_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.
- D. Create a sales_transaction table that stores the sales_transaction_header and sales_transaction_line data as a JSON data type.
Answer: A
NEW QUESTION # 189
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 future growth of the dataset. Which database and data model should you choose?
- A. 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
- B. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
- C. Create a table in BigQuery, and append the new samples for CPU and memory to the table
- D. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
Answer: B
NEW QUESTION # 190
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?
- A. Limit the query columns being pulled in the final result.
- B. Create a materialized view based off of the query being run.
- C. Use a cached query to accelerate time to results.
- D. Run a scheduled query to pull the necessary data at specific intervals daily.
Answer: B
NEW QUESTION # 191
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?
- A. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline
3 Schedule the DAG to run hourly - B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
2 Create a separate DAG for each table that needs to go through the pipeline
3 Schedule the DAGs to run hourly - C. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline.
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG - D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2 Create a separate DAG for each table that needs to go through the pipeline
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
Answer: D
Explanation:
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performance of these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for the corresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. Reference:
1: Dataproc Operator | Cloud Composer | Google Cloud
2: BigQuery Operator | Cloud Composer | Google Cloud
3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
[5]: Triggering DAGs | Cloud Composer | Google Cloud
[6]: Cloud Storage Operator | Cloud Composer | Google Cloud
NEW QUESTION # 192
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 dat a. What should you do?
- A. Adopt multi-regional Cloud Storage buckets in your architecture.
- 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 two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.
Answer: B
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:
Google Cloud Storage Dual-Region
Turbo Replication in Google Cloud Storage
NEW QUESTION # 193
Your company's customer and order databases are often under heavy load. This makes performing
analytics against them difficult without harming operations. The databases are in a MySQL cluster, with
nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?
- A. Use an ETL tool to load the data from MySQL into Google BigQuery.
- B. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
- C. Add a node to the MySQL cluster and build an OLAP cube there.
- D. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
Answer: D
NEW QUESTION # 194
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