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Latest Google Certified Professional Data Engineer Exam vce dumps & Professional-Data-Engineer prep4sure exam

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Google Professional-Data-Engineer Exam is intended for professionals who work with data engineering, data integration, or data analysis. Professional-Data-Engineer exam tests the candidate's knowledge and understanding of Google Cloud Platform tools and services, including BigQuery, Cloud Dataflow, Cloud Pub/Sub, Cloud Storage, and more. Professional-Data-Engineer exam consists of multiple-choice questions and practical scenarios that test the candidate's ability to apply their knowledge and skills to real-world problems. Passing the exam and obtaining the certification demonstrates the individual's proficiency in designing and implementing scalable and reliable data processing systems using Google Cloud Platform technologies.

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Google Certified Professional Data Engineer Exam Sample Questions (Q335-Q340):

NEW QUESTION # 335
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Windows
B. Sessions
C. Triggers
D. OutputCriteria

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.

NEW QUESTION # 336
Your company built a TensorFlow neural-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. Dimensionality Reduction
B. Threading
C. Dropout Methods
D. Serialization

Answer: C

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

NEW QUESTION # 337
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

A. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
B. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
C. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
D. Increase the size of your parquet files to ensure them to be 1 GB minimum.

Answer: B

Explanation:
To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here's why option C is the best choice:
Performance of SSDs:
SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.
Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.
Intermediate Storage with HDFS:
Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.
HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.
Cost Considerations:
Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.
Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.

NEW QUESTION # 338
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. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
B. Add a node to the MySQL cluster and build an OLAP cube there.
C. Use an ETL tool to load the data from MySQL into Google BigQuery.
D. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

Answer: D

NEW QUESTION # 339
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.)

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

Answer: B,E,F

Explanation:
Explanation/Reference:

NEW QUESTION # 340
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Bob Shaw Bob Shaw

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