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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Topic 2: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Orchestrating data workflows 2. Transforming and cleaning data 3. Ingesting data from various sources - Deploying and managing systems 1. Monitoring and logging data processes 2. Managing infrastructure and resources
Topic 3: Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Topic 4: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads
Topic 5: Designing data processing systems	20%	- Designing for business requirements 1. Designing for scalability and elasticity 2. Selecting appropriate storage solutions 3. Designing for reliability and fault tolerance - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance

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Google Certified Professional Data Engineer Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. Python using MapReduce
B. Java using MapReduce
C. HiveQL using Hive
D. PigLatin using Pig

Answer: A

NEW QUESTION # 152
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

A. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
B. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.
C. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.
D. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Answer: C

Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. Reference:
Signed URLs | Cloud Storage Documentation
V4 signing process with Cloud Storage tools
V4 signing process with your own program
Using a URL list file
What Is a 403 Forbidden Error (and How Can I Fix It)?

NEW QUESTION # 153
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery.
Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Cluster table data by create_date, partition by location and device_version
B. Partition table data by create_date, location_id and device_version
C. Partition table data by create_date cluster table data by tocation_id and device_version
D. Cluster table data by create_date location_id and device_version

Answer: D

NEW QUESTION # 154
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

A. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
B. Deploy small Kafka clusters in your data centers to buffer events.
C. Have the data acquisition devices publish data to Cloud Pub/Sub.
D. Establish a Cloud Interconnect between all remote data centers and Google.

Answer: B

NEW QUESTION # 155
How can you get a neural network to learn about relationships between categories in a categorical feature?

A. Create a multi-hot column
B. Create a one-hot column
C. Create a hash bucket
D. Create an embedding column

Answer: D

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-dee

NEW QUESTION # 156
......

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