Free Access Google.Professional-Data-Engineer.v2024-12-09.q327 Practice Test (Page 9)

Question 36

Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A.Preemptible workers cannot use persistent disk.

B.Preemptible workers cannot store data.

C.If a preemptible worker is reclaimed, then a replacement worker must be added manually.

D.A Dataproc cluster cannot have only preemptible workers.

Question 37

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A.They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

B.They have not assigned the timestamp, which causes the job to fail

C.They have not applied a global windowing function, which causes the job to fail when the pipeline is created

D.They have not set the triggers to accommodate the data coming in late, which causes the job to fail

Question 38

Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?

A.Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.

B.Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

C.Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

D.Add a try... catch block to your sideOutput to create a PCollection that can be stored to PubSub later.

Question 39

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?

A.Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

B.Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

C.Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

D.Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

Question 40

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly.
What method can you employ to address this?

A.Threading

B.Serialization

C.Dropout Methods

D.Dimensionality Reduction

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Question 36

Question 37

Question 38

Question 39

Question 40

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