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

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Google Certified Professional Data Engineer Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 3090 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Modify your pipeline to maintain the last 3090 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
B. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
C. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
D. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

NEW QUESTION # 141
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.2. Load the booking and user profile data into a BigQuery table.
B. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the default masking value as the data masking rule.3. Assign the policy to the email field in both tables.4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
C. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.2. Load the booking and user profile data into a BigQuery table.
D. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the email mask as the data masking rule.3. Assign the policy to the email field in both tables. A4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

Answer: A

Explanation:
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by usingrecordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them.
You can then load the de-identified data into a BigQuery table for analysis. References:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

NEW QUESTION # 142
Which of the following are feature engineering techniques? (Select 2 answers)

A. Bucketization of a continuous feature
B. Hidden feature layers
C. Crossed feature columns
D. Feature prioritization

Answer: A,C

Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins
/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model

NEW QUESTION # 143
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have
noticed extremely poor performance. How should the row key be redesigned to improve Bigtable
performance on queries that populate real-time dashboards?

A. Use a row key of the form <sensorid>.
B. Use a row key of the form <timestamp>.
C. Use a row key of the form >#<sensorid>#<timestamp>.
D. Use a row key of the form <timestamp>#<sensorid>.

Answer: B

NEW QUESTION # 144
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum.
What should you do?

A. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
B. Move your data to Cloud Storage. Run your jobs on Dataproc.
C. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
D. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Answer: B

Explanation:
Dataproc's Compatibility with Apache Spark:Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake:Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead:By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes:This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A). Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances:This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C). Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach:While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D). Rewrite your jobs in Apache Beam. Run your jobs in Dataflow:Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.

NEW QUESTION # 145
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