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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost - Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data
Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance - Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance
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

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Google Certified Professional Data Engineer Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

A. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
B. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
D. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.

Answer: D

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
References:
Connected Sheets | Google Sheets | Google for Developers
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs

NEW QUESTION # 16
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
C. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
D. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Answer: D

NEW QUESTION # 17
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have different features from the mutated samples in the database.
B. There are roughly equal occurrences of both normal and mutated samples in the database.
C. There are very few occurrences of mutations relative to normal samples.
D. You expect future mutations to have similar features to the mutated samples in the database.
E. You already have labels for which samples are mutated and which are normal in the database.

Answer: C,D

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection

NEW QUESTION # 18
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Google Cloud Dataflow job to process the data.
B. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
C. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Answer: D

Explanation:
Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.

NEW QUESTION # 19
Which of these is not a supported method of putting data into a partitioned table?

A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
B. Create a partitioned table and stream new records to it every day.
C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

Answer: C

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.

NEW QUESTION # 20
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