Google Professional-Data-Engineer Exam Questions Vce & Exam Questions Professional-Data-Engineer Vce

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cluster sizing and autoscaling 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cost optimization - Designing data pipelines 1. AI data enrichment 2. Batch processing 3. Data acquisition and import 4. Integrating with new data sources 5. Streaming (e.g., windowing, late arriving data) 6. Processing logic - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements
Topic 2: Storing the data (~20% of the exam)	20%	- Using a data lake 1. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls) - 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 - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance - Planning for using a data warehouse 1. Mapping business requirements 2. Defining architecture to support data access patterns 3. Deciding the degree of data normalization 4. Designing the data model
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Identity and Access Management (IAM) 2. Data encryption 3. Auditing, privacy, and compliance - Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows)
Topic 4: Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Automating data processes 1. Workflow orchestration 2. Scheduling jobs 3. Continuous integration and continuous deployment (CI/CD)
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 (Q92-Q97):

NEW QUESTION # 92
You work for a large bank that operates in locations throughout North America. You are setting up a data storage system that will handle bank account transactions. You require ACID compliance and the ability to access data with SQL. Which solution is appropriate?

A. Store transaction data in Cloud Spanner. Enable stale reads to reduce latency.
B. Store transaction in Cloud Spanner. Use locking read-write transactions.
C. Store transaction data in BigQuery. Disabled the query cache to ensure consistency.
D. Store transaction data in Cloud SQL. Use a federated query BigQuery for analysis.

Answer: C

NEW QUESTION # 93
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. WHERE
B. SELECT
C. LIMIT
D. BETWEEN

Answer: B

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by
BigQuery.

NEW QUESTION # 94
You manage your company's BigQuery data warehouse. You need to implement a solution that enables the data science team to modify data for experiments without affecting the original tables, while minimizing additional storage costs. What should you do?

A. Create table clones of all the tables for the data science team to use.
B. Set up authorized views in a shared dataset that reference the original tables.
C. Create snapshots of all the tables and restore them for the data science team to use.
D. Create a separate dataset with full copies of all the tables for each member of the data science team.

Answer: A

Explanation:
BigQuery Table Clones are specifically designed for the use case where you need a writable copy of a table that is storage-efficient.
* Writable and Independent: Unlike views (which are read-only) or snapshots (which are read-only until restored), a table clone is a lightweight, writable copy. The data science team can perform DML operations (INSERT, UPDATE, DELETE) on the clone without those changes reflecting in the production base table.
* Minimal Storage Costs: Table clones use a copy-on-write mechanism. Initially, the clone consumes zero additional storage because it points to the same underlying physical data blocks as the base table.
You are only billed for the data that differs between the clone and the base table (i.e., new or modified rows in the clone).
* Correcting other options:
* A (Authorized Views): Views do not allow the data science team to modify or "experiment" with the data; they only allow querying of the existing production data.
* B (Snapshots): Snapshots are read-only. To modify them, they must be restored into a standard table, at which point you are charged for the full storage of that restored table, failing the
"minimize storage costs" requirement.
* D (Full Copies): This is the most expensive option as it duplicates all physical data, leading to significantly higher storage costs.
Reference: Google Cloud Documentation on BigQuery Table Clones:
"A table clone is a lightweight, writable copy of another table (called the base table). You are only charged for storage of data in the table clone that differs from the base table, so initially there is no storage cost for a table clone... Common use cases include: Creating sandboxes for users to generate their own analytics and data manipulations, without physically copying all of the production data." (Source: Introduction to table clones)

NEW QUESTION # 95
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the TABLE_DATE_RANGEfunction
B. Use the WHERE_PARTITIONTIMEpseudo column
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD

Answer: A

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to- understand-your-mobile-app?hl=am

NEW QUESTION # 96
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?

A. multi-keyed data with very high latency
B. single-keyed data with very low latency
C. multi-keyed data with very low latency
D. single-keyed data with very high latency

Answer: B

Explanation:
Explanation
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 97
......

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