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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing machine learning models	26%	- ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Designing data processing systems	22%	- Batch and streaming data processing design 1. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs - Data architecture and storage design 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) 2. Designing scalable and cost-effective data models
Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Ensuring solution quality	28%	- Security and governance 1. Data governance and compliance 2. IAM and access control in GCP - Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies

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Google Certified Professional Data Engineer Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
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. 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. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
D. 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.

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 using recordTransformations 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 # 74
Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?

A. Implement row-level security in BigQuery to restrict data access and reduce the amount of data processed by the queries.
B. Configure Looker Studio to use a shorter data refresh interval to ensure fresh data is always displayed.
C. Use BigQuery BI Engine to accelerate query performance by caching frequently accessed data.
D. Create a materialized view in BigQuery that pre-calculates the aggregations and filters used in the Looker Studio dashboards.

Answer: C,D

Explanation:
Pre-calculating commonly used aggregations and filters in a BigQuery materialized view significantly reduces the amount of computation required at query time, which directly improves dashboard responsiveness. Enabling BigQuery BI Engine further accelerates performance by keeping frequently accessed data in memory, optimizing interactive queries from Looker Studio and reducing latency for complex analytical workloads.

NEW QUESTION # 75
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 Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.
B. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.

Answer: A

NEW QUESTION # 76
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 1 TB
B. 500 GB
C. 500 TB
D. 1 GB

Answer: A

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions.
It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

NEW QUESTION # 77
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use feature engineering to add features for eyes, noses, and mouths to the input data.
B. Use K-means Clustering to detect faces in the pixels.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 78
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