Free Professional-Data-Engineer Exam Braindumps

- (Exam Topic 1)
Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

1. A. Put the data into Google Cloud Storage.
2. B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
3. C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
4. D. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Correct Answer: B

- (Exam Topic 1)
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?

1. A. Threading
2. B. Serialization
3. C. Dropout Methods
4. D. Dimensionality Reduction

Correct Answer: C
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

- (Exam Topic 1)
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

1. A. Supervised learning to determine which transactions are most likely to be fraudulent.
2. B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
3. C. Clustering to divide the transactions into N categories based on feature similarity.
4. D. Supervised learning to predict the location of a transaction.
5. E. Reinforcement learning to predict the location of a transaction.
6. F. Unsupervised learning to predict the location of a transaction.

Correct Answer: BCE

- (Exam Topic 1)
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?

1. A. Issue a command to restart the database servers.
2. B. Retry the query with exponential backoff, up to a cap of 15 minutes.
3. C. Retry the query every second until it comes back online to minimize staleness of data.
4. D. Reduce the query frequency to once every hour until the database comes back online.

Correct Answer: B

- (Exam Topic 5)
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

1. A. A sequential numeric ID
2. B. A timestamp followed by a stock symbol
3. C. A non-sequential numeric ID
4. D. A stock symbol followed by a timestamp

Correct Answer: AB
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti