ADP Hot Spot Questions - ADP Valid Test Pattern

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Google ADP Exam Syllabus Topics:

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

>> ADP Hot Spot Questions <<

Google Associate Data Practitioner Sample Questions (Q52-Q57):

NEW QUESTION # 52
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C. Use Dataflow to transform the data and write the transformed data to BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: C

NEW QUESTION # 53
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
B. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
C. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
D. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

Answer: C

NEW QUESTION # 54
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Answer: D

NEW QUESTION # 55
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
C. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
D. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

Answer: B

NEW QUESTION # 56
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
D. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.

Answer: C

NEW QUESTION # 57
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ADP Hot Spot Questions - ADP Valid Test Pattern

Google ADP Exam Syllabus Topics:

Top ADP Hot Spot Questions - Unparalleled & Useful ADP Materials Free Download for Google ADP Exam

Google Associate Data Practitioner Sample Questions (Q52-Q57):