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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76

A. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
C. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
D. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
E. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

Answer: D

NEW QUESTION # 77
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
B. The [myproject:mydataset.mytable] table has too many partitions
C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
D. Users are running too many concurrent queries in the system

Answer: D

NEW QUESTION # 78
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

A. Increase the share of the test sample in the train-test split.
B. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
D. Try to collect more data and increase the size of your dataset.

Answer: B

NEW QUESTION # 79
You are designing a data lake on Google Cloud to store vast amounts of customer interaction data from various sources, such as websites, mobile apps, and social media. You need to ensure that this data, which arrives in different formats, is consistently cataloged and easy for data analysts to discover and use. You also want to perform basic data quality checks and transformations before the data is consumed by downstream applications. You need an automated and managed data governance solution. What should you do?

A. Use Cloud Storage and BigQuery as repositories. Use Dataplex Universal Catalog for metadata discovery, data quality checks, and transformations.
B. Use Cloud Storage as the central repository. Use Vertex AI to classify and process the data and perform data quality checks.
C. Stream all the data directly into BigQuery, where it is automatically cataloged and governed.
D. Use Cloud Storage as the central repository. Use a Cloud Run function to catalog, transform the data, and perform data quality checks.

Answer: A

Explanation:
Dataplex provides a managed data governance solution with a unified catalog, automated metadata discovery, and support for data quality checks and transformations. Using Cloud Storage and BigQuery as repositories allows analysts to discover and consume data consistently across the data lake.

NEW QUESTION # 80
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

A. Parameter servers
B. Workers
C. Workers and parameter servers
D. Masters, workers, and parameter servers

Answer: C

Explanation:
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.

NEW QUESTION # 81
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