Top Features of ExamDiscuss Professional-Data-Engineer PDF Questions and Practice Test Software

Top Features of ExamDiscuss Professional-Data-Engineer PDF Questions and Practice Test Software


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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

  • Verification and monitoring
  • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  • Resizing and autoscaling resources
  • Designing for security and compliance
  • Building and running test suites
  • Pipeline monitoring (e.g., Stackdriver)
  • Ensuring privacy (e.g., Data Loss Prevention API)
  • Identity and access management (e.g.,Cloud IAM)
  • Data staging, cataloging, and discovery

Google Professional-Data-Engineer Certification is a highly recognized credential in the field of data engineering. It is designed to demonstrate a candidate’s expertise in designing, building, and maintaining data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for experienced data professionals who are seeking to enhance their skills and knowledge in the areas of data engineering, cloud computing, and data analytics.

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Google Certified Professional Data Engineer Exam Sample Questions (Q122-Q127):

NEW QUESTION # 122

Which of these is NOT a way to customize the software on Dataproc cluster instances?

  • A. Set initialization actions
  • B. Configure the cluster using Cloud Deployment Manager
  • C. Log into the master node and make changes from there
  • D. Modify configuration files using cluster properties

Answer: B

Explanation:

You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.

You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.

When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


NEW QUESTION # 123

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network.

You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible.

What should you do?

  • A. Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.
  • B. Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
  • C. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
  • D. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.

Answer: D

Explanation:

Here's a detailed breakdown of why this solution is optimal and why others fall short:

Why Option C is the Best Solution:

* Kafka Connect Bridge:This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.

* Pub/Sub as a Buffer:Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.

* Custom Dataflow Pipeline:Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.

* Minimal Latency:By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.

Why Other Options Are Not Ideal:

* Option A:Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.

* Option B:While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.

* Option D:Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.

Additional Considerations:

* Schema Management:Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.

* Monitoring:Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.

By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high- throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.


NEW QUESTION # 124

Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

  • A. Fire
  • B. Ignite
  • C. Blaze
  • D. Spark

Answer: D

Explanation:

Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.

Reference: https://cloud.google.com/dataproc/docs/


NEW QUESTION # 125

Why do you need to split a machine learning dataset into training data and test data?

  • A. To allow you to create unit tests in your code
  • B. To make sure your model is generalized for more than just the training data
  • C. So you can use one dataset for a wide model and one for a deep model
  • D. So you can try two different sets of features

Answer: B

Explanation:

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/


NEW QUESTION # 126

You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.

What should you do?

  • A. Convert your batch BQ queries into interactive BQ queries.
  • B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
  • C. Create an additional project to overcome the 2K on-demand per-project quota.
  • D. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Answer: D

Explanation:

Explanation

Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery


NEW QUESTION # 127

......

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