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Google Certified Professional Data Engineer Exam Sample Questions (Q257-Q262):
NEW QUESTION # 257
What are two of the benefits of using denormalized data structures in BigQuery?
- A. Reduces the amount of data processed, increases query speed
- B. Reduces the amount of data processed, reduces the amount of storage required
- C. Increases query speed, makes queries simpler
- D. Reduces the amount of storage required, increases query speed
Answer: C
Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION # 258
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?
- A. Cloud Dataflow, Cloud SQL, Cloud Spanner
- B. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
- C. Cloud Pub/Sub, Cloud Storage, BigQuery
- D. Cloud Dataproc, Cloud Dataflow, BigQuery
Answer: C
NEW QUESTION # 259
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?
- A. Tier older data onto Cloud Storage files, and leverage extended tables.
- B. Re-create the table using data partitioning on the package delivery date.
- C. Implement clustering in BigQuery on the package-tracking ID column.
- D. Implement clustering in BigQuery on the ingest date column.
Answer: D
NEW QUESTION # 260
You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?
- A. Job validation
- B. Graph or pipeline construction
- C. Insufficient permissions
- D. Exceptions in worker code
Answer: D
Explanation:
https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#detect_an_exception_in_worker_c While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.
NEW QUESTION # 261
Which of the following are examples of hyperparameters? (Select 2 answers.)
- A. Number of hidden layers
- B. Weights
- C. Number of nodes in each hidden layer
- D. Biases
Answer: A,C
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview
NEW QUESTION # 262
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