ADP Trainingsmaterialien: Associate Data Practitioner & ADP Lernmittel & Google ADP Quiz

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- Data extraction and transfer tools 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service 2. Cloud Data Fusion, Storage Transfer Service - Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Storage solutions selection 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal
Topic 2: Data Analysis and Presentation	27%	- Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Data exploration and analysis 1. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data
Topic 3: Data Management and Governance	25%	- Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging
Topic 4: Data Pipeline Orchestration	18%	- Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements - Pipeline automation and scheduling 1. Schedule and monitor data jobs 2. Error handling and monitoring

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q14-Q19):

14. Frage
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Antwort: C

15. Frage
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. Dataflow BigQuery
B. Dataproc Serverless Bigtable
C. Cloud Composer Cloud SQL for MySQL
D. BigQuery Analytics Hub

Antwort: A

16. Frage
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C. Apply a filter to only show products with a positive profit margin.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Antwort: B

17. Frage
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
B. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
C. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
D. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

Antwort: A

18. Frage
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

Antwort: B

19. Frage
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