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Preparation Process
To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.
Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.
Google Professional-Data-Engineer Certification Exam is designed for professionals seeking to demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, data architects, and data analysts who work with big data solutions. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and build data processing systems, as well as manage and monitor them in a production environment.
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Google Certified Professional Data Engineer Exam Sample Questions (Q204-Q209):
NEW QUESTION # 204
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?
Answer: B
Explanation:
Explanation
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun
NEW QUESTION # 205
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)
Answer: A,B
NEW QUESTION # 206
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old dat a. The current business requirements are:
* The old data can be deleted anytime
* You plan to use the visualization layer for current and historical reporting
* The old data should be available instantly when accessed
* There should not be any charges for data retrieval.
What should you do to optimize for cost?
Answer: C
Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.
NEW QUESTION # 207
How can you get a neural network to learn about relationships between categories in a categorical feature?
Answer: A
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html
NEW QUESTION # 208
Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)
Answer: C,D,E
NEW QUESTION # 209
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