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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Streaming (e.g., windowing, late arriving data) 2. Integrating with new data sources 3. Batch processing 4. AI data enrichment 5. Data acquisition and import 6. Processing logic - Designing data processing resources 1. Cost optimization 2. Cluster sizing and autoscaling 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) - Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Topic 3: Storing the data (~20% of the exam)	20%	- Selecting storage systems 1. Planning for storage costs and performance 2. Lifecycle management of data 3. Analyzing data access patterns - Planning for using a data warehouse 1. Mapping business requirements 2. Designing the data model 3. Deciding the degree of data normalization 4. Defining architecture to support data access patterns - Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems - Using a data lake 1. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls)
Topic 4: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Auditing, privacy, and compliance 2. Data encryption 3. Identity and Access Management (IAM) - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle - Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows)
Topic 5: Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools - Sharing data securely 1. Data sharing and collaboration 2. Publishing datasets

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Google Certified Professional Data Engineer Exam Sample Questions (Q344-Q349):

NEW QUESTION # 344
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.)

A. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
B. Introduce data compression for each file to increase the rate file of file transfer.
C. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
E. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Answer: C,D

NEW QUESTION # 345
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat
a. How can you adjust your application design?

A. Convert the streaming insert code to batch load for individual messages.
B. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
C. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
D. Re-write the application to load accumulated data every 2 minutes.

Answer: C

Explanation:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue. So We need to wait till it's written tio storage

NEW QUESTION # 346
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.
B. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.
C. Store your data in BigQuery. Use the metric as a primary key.
D. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

Answer: A

Explanation:
To store your data in a way that meets both access patterns, you should:
* A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
* B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs.
BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
* C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
* D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.

NEW QUESTION # 347
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. Unsupervised learning to predict the location of a transaction.
B. Reinforcement learning to predict the location of a transaction.
C. Clustering to divide the transactions into N categories based on feature similarity.
D. Unsupervised learning to determine which transactions are most likely to be fraudulent.
E. Supervised learning to predict the location of a transaction.
F. Supervised learning to determine which transactions are most likely to be fraudulent.

Answer: B,C,D

NEW QUESTION # 348
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

A. Clear your browser history for the past hour then reload the tab showing the virtualizations.
B. Refresh your browser tab showing the visualizations.
C. Disable caching by editing the report settings.
D. Disable caching in BigQuery by editing table details.

Answer: C

Explanation:
Explanation/Reference: https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION # 349
......

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