Free Professional-Data-Engineer Exam Braindumps

- (Exam Topic 6)
You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

1. A. Call the Cloud Natural Language API from your applicatio
2. B. Process the generated Entity Analysis as labels.
3. C. Call the Cloud Natural Language API from your applicatio
4. D. Process the generated Sentiment Analysis as labels.
5. E. Build and train a text classification model using TensorFlo
6. F. Deploy the model using Cloud Machine Learning Engin
7. G. Call the model from your application and process the results as labels.
8. H. Build and train a text classification model using TensorFlo
9. I. Deploy the model using a KubernetesEngine cluste
10. J. Call the model from your application and process the results as labels.

Correct Answer: B

- (Exam Topic 6)
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. A. Store and process the entire dataset in BigQuery.
2. B. Store and process the entire dataset in Cloud Bigtable.
3. C. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
4. D. Store the warm data as files in Cloud Storage, and store the active data in BigQuer
5. E. Keep this ratio as 80% warm and 20

Correct Answer: D

- (Exam Topic 6)
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

1. A. Use Cloud Dataproc to run your transformation
2. B. Monitor CPU utilization for the cluste
3. C. Resize the number of worker nodes in your cluster via the command line.
4. D. Use Cloud Dataproc to run your transformation
5. E. Use the diagnose command to generate an operational output archiv
6. F. Locate the bottleneck and adjust cluster resources.
7. G. Use Cloud Dataflow to run your transformation
8. H. Monitor the job system lag with Stackdrive
9. I. Use the default autoscaling setting for worker instances.
10. J. Use Cloud Dataflow to run your transformation
11. K. Monitor the total execution time for a sampling of job
12. L. Configure the job to use non-default Compute Engine machine types when needed.

Correct Answer: B

- (Exam Topic 4)
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

1. A. The CSV data loaded in BigQuery is not flagged as CSV.
2. B. The CSV data has invalid rows that were skipped on import.
3. C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
4. D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Correct Answer: B

- (Exam Topic 6)
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 to 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. A. Apache Kafka
2. B. Cloud Storage
3. C. Cloud Pub/Sub
4. D. Firebase Cloud Messaging

Correct Answer: A