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ACA Big Data study materials
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ACA Big Data practice questions

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Question 1Big Data Fundamentals and Alibaba Cloud Big Data Products

Which characteristic of big data refers to the wide variety of data formats, including structured, semi-structured, and unstructured data?

  1. Velocity
  2. Volume
  3. Veracity
  4. Variety Correct answer.

Explanation

The classic big data dimensions describe different challenges. Variety concerns the range of data types and formats that must be ingested and processed, not how fast or how much data arrives.

Question 2Big Data Fundamentals and Alibaba Cloud Big Data Products

A retail company wants to store massive volumes of raw log data cheaply and run SQL-like analytical queries over it later. Which Alibaba Cloud service is designed primarily for this purpose?

  1. Alibaba Cloud CDN
  2. MaxCompute Correct answer.
  3. Alibaba Cloud WAF
  4. Alibaba Cloud DNS

Explanation

MaxCompute is Alibaba Cloud's large-scale data warehousing and batch computing service, suited to storing huge datasets and running SQL analytics over them. The other options are network and security services, not analytical data platforms.

Question 3Big Data Fundamentals and Alibaba Cloud Big Data Products

Which statement best describes the role of a data lake?

  1. It is limited to structured transaction records from operational systems.
  2. It enforces strict schema-on-write validation for every incoming record.
  3. It stores data in a fully normalized relational schema before any analysis.
  4. It stores large volumes of raw data in native formats until it is needed for analysis. Correct answer.

Explanation

A data lake holds raw data in its native format and applies schema when the data is read, which supports diverse analytical workloads. It does not require pre-normalization or schema-on-write like a traditional warehouse.

Question 4Big Data Fundamentals and Alibaba Cloud Big Data Products

In a distributed computing framework, what is the primary purpose of partitioning input data?

  1. To encrypt each record before processing
  2. To compress the data for cheaper storage
  3. To enable parallel processing across multiple nodes Correct answer.
  4. To convert unstructured data into relational tables

Explanation

Partitioning splits a dataset into independent chunks that can be processed concurrently on different nodes. This parallelism is what lets distributed frameworks scale with data size.

Question 5Big Data Fundamentals and Alibaba Cloud Big Data Products

Which Alibaba Cloud service is a fully managed, real-time stream processing platform built on Apache Flink?

  1. ApsaraDB for Redis
  2. Realtime Compute for Apache Flink Correct answer.
  3. Object Storage Service
  4. Alibaba Cloud Elasticsearch

Explanation

Realtime Compute for Apache Flink is Alibaba Cloud's managed stream processing service based on Flink. It is purpose-built for continuous, low-latency processing of streaming data.

Question 6Big Data Fundamentals and Alibaba Cloud Big Data Products

What does the term ETL stand for in data integration?

  1. Evaluate, Test, Launch
  2. Encode, Transfer, Link
  3. Extract, Transform, Load Correct answer.
  4. Export, Track, Log

Explanation

ETL is the classic pipeline pattern: data is extracted from sources, transformed into a usable form, and loaded into a target store. This sequence underpins much of data warehousing work.

Question 7Big Data Fundamentals and Alibaba Cloud Big Data Products

Which data type is most characteristic of semi-structured data?

  1. A CSV file with a strictly enforced column schema
  2. A JSON document with nested key-value fields Correct answer.
  3. A fixed-width binary record in a mainframe file
  4. A plain-text paragraph with no delimiters

Explanation

Semi-structured data has some organizing markers, such as tags or keys, but does not fit a rigid tabular schema. JSON documents with nested keys are a standard example.

Question 8Big Data Fundamentals and Alibaba Cloud Big Data Products

In the context of big data processing, what distinguishes batch processing from stream processing?

  1. Batch processing only works on unstructured data, while stream processing only works on structured data.
  2. Batch processing runs only on a single machine, while stream processing always runs on a cluster.
  3. Batch processing handles bounded datasets over intervals, while stream processing handles unbounded data continuously. Correct answer.
  4. Batch processing requires no storage, while stream processing always persists all records.

Explanation

Batch jobs operate on finite, bounded inputs collected over a period, whereas stream processing consumes continuous, unbounded event flows as they arrive. The distinction is about bounded versus unbounded data, not storage or data type.

Question 9Big Data Fundamentals and Alibaba Cloud Big Data Products

A data engineer needs a managed service to orchestrate scheduled ETL workflows that move data between Alibaba Cloud storage and compute services. Which service fits this need?

  1. Alibaba Cloud NAT Gateway
  2. ApsaraDB for MongoDB
  3. Alibaba Cloud Anti-DDoS
  4. DataWorks Correct answer.

Explanation

DataWorks provides data integration, workflow scheduling, and data development capabilities for orchestrating ETL pipelines. It is the platform layer that coordinates movement and transformation across services.

Question 10Big Data Fundamentals and Alibaba Cloud Big Data Products

Which Alibaba Cloud storage service is best suited for storing large volumes of unstructured objects such as images, videos, and backups?

  1. Alibaba Cloud Table Store
  2. ApsaraDB for RDS
  3. Object Storage Service Correct answer.
  4. Alibaba Cloud PolarDB

Explanation

Object Storage Service stores data as objects in buckets, making it appropriate for large unstructured files like media and backups. Relational and table databases are intended for structured records.

Question 11Big Data Fundamentals and Alibaba Cloud Big Data Products

In MaxCompute, what is a table partition used for?

  1. To group rows by a column value so that queries can scan less data Correct answer.
  2. To restrict which users may query the table
  3. To automatically convert the table into a streaming source
  4. To physically replicate the table across regions

Explanation

Partitioning organizes table data by a column value, such as date, so queries that filter on that column can skip irrelevant partitions. This reduces the amount of data scanned and improves performance and cost.

Question 12Big Data Fundamentals and Alibaba Cloud Big Data Products

A team is ingesting clickstream events that must be processed with sub-second latency to trigger alerts. Which processing approach is most appropriate?

  1. A nightly batch job that reprocesses the full day's logs
  2. Stream processing with a windowed aggregation Correct answer.
  3. A weekly ETL export to a data warehouse
  4. Manual ad hoc SQL queries run on demand

Explanation

Sub-second alerting requires continuous processing as events arrive, which stream processing with windowed aggregation provides. Batch or on-demand approaches introduce latency that violates the requirement.

Question 13Big Data Fundamentals and Alibaba Cloud Big Data Products

Which concept in distributed data processing means tolerating node failures without losing the overall job?

  1. Data skew
  2. Schema evolution
  3. Fault tolerance Correct answer.
  4. Columnar storage

Explanation

Fault tolerance is the property that lets a system continue operating correctly when components fail, often via replication or recomputation. It is distinct from schema, skew, and storage layout concerns.

Question 14Big Data Fundamentals and Alibaba Cloud Big Data Products

What does the term 'data skew' describe in a distributed processing job?

  1. Data that has been corrupted during transfer
  2. Data that is stored in multiple compressed formats
  3. Data that is unevenly distributed across partitions, overloading some tasks Correct answer.
  4. Data whose schema changes between records

Explanation

Data skew occurs when key values are distributed unevenly, so a few partitions receive far more data than others and become bottlenecks. This imbalance degrades job performance even when total data volume is modest.

Question 15Big Data Fundamentals and Alibaba Cloud Big Data Products

Which Alibaba Cloud service provides a managed Hadoop and Spark environment for big data processing?

  1. Alibaba Cloud E-MapReduce Correct answer.
  2. ApsaraDB for Redis
  3. Alibaba Cloud Message Queue
  4. Alibaba Cloud SLB

Explanation

E-MapReduce is Alibaba Cloud's managed big data platform built around Hadoop and Spark ecosystems. It handles cluster provisioning and management for these open-source frameworks.

Question 16Big Data Fundamentals and Alibaba Cloud Big Data Products

In a data warehouse, what is the main purpose of a slowly changing dimension (SCD) technique?

  1. To compress dimension tables for storage savings
  2. To track historical changes to dimension attributes over time Correct answer.
  3. To shard fact tables across multiple servers
  4. To encrypt sensitive dimension columns

Explanation

Slowly changing dimensions preserve history when attribute values change, for example by adding new rows or versioning. This lets analysts query the state of a dimension as of a past point in time.

Question 17Big Data Fundamentals and Alibaba Cloud Big Data Products

A company needs to move data from an on-premises database into MaxCompute on a recurring schedule. Which combination of Alibaba Cloud capabilities is most appropriate?

  1. CDN plus DNS for data replication
  2. Anti-DDoS plus WAF for data transfer
  3. DataWorks Data Integration with scheduled workflows Correct answer.
  4. SLB plus NAT Gateway for data loading

Explanation

DataWorks Data Integration provides connectors for moving data between sources and targets, and its scheduling supports recurring runs. This combination addresses both the transfer and the periodic orchestration requirement.

Question 18Big Data Fundamentals and Alibaba Cloud Big Data Products

Which statement accurately describes schema-on-read?

  1. The schema is applied only when the data is queried or read. Correct answer.
  2. The schema is stored separately in a metadata catalog and never used at query time.
  3. The schema is fixed at table creation and cannot change.
  4. The schema is validated and enforced when data is written to storage.

Explanation

Schema-on-read defers structure interpretation until the data is accessed, which suits raw and evolving datasets. This contrasts with schema-on-write, where validation happens at ingestion.

Question 19Big Data Fundamentals and Alibaba Cloud Big Data Products

What is the primary role of a message queue in a big data architecture?

  1. To compile SQL queries into execution plans
  2. To enforce referential integrity across tables
  3. To permanently store analytical results for reporting
  4. To decouple producers from consumers and buffer event streams Correct answer.

Explanation

Message queues buffer events so producers and consumers can operate independently and at different rates. This decoupling improves resilience and smooths bursts in streaming architectures.

Question 20Big Data Fundamentals and Alibaba Cloud Big Data Products

A team notices that a MapReduce-style job runs slowly because one reduce task processes far more records than the others. Which remedy most directly addresses this?

  1. Adding more compression to the output
  2. Reducing the number of mapper tasks
  3. Increasing the replication factor of the input files
  4. Repartitioning the data with a better-distributed key or salting skewed keys Correct answer.

Explanation

The slowdown comes from skew in the key distribution, which concentrates records on a few reducers. Changing the partitioning key or adding a salt spreads records more evenly and relieves the bottleneck.

Question 21Big Data Fundamentals and Alibaba Cloud Big Data Products

Which Alibaba Cloud service is a managed search and analytics engine commonly used for log analysis and full-text search?

  1. Alibaba Cloud Elasticsearch Correct answer.
  2. ApsaraDB for RDS
  3. Alibaba Cloud Function Compute
  4. Alibaba Cloud SLS only for billing

Explanation

Alibaba Cloud Elasticsearch provides managed search and analytics capabilities suited to log analysis and full-text queries. It is built for indexing and querying large text and document collections.

Question 22Big Data Fundamentals and Alibaba Cloud Big Data Products

Which scenario is the best fit for using MaxCompute rather than a traditional OLTP relational database?

  1. Serving low-latency point lookups for a mobile app's user profile
  2. Handling thousands of small concurrent order-insert transactions per second
  3. Enforcing row-level locking during checkout operations
  4. Running complex aggregations over terabytes of historical data Correct answer.

Explanation

MaxCompute is optimized for large-scale batch analytics and complex aggregations over massive datasets. OLTP workloads with frequent small transactions and point lookups are better served by relational databases.

Question 23Big Data Fundamentals and Alibaba Cloud Big Data Products

In a Lambda architecture, what role does the batch layer play relative to the speed layer?

  1. It replaces the serving layer for all queries.
  2. It stores only the most recent events within a short time window.
  3. It recomputes accurate views over the full dataset to correct the speed layer's approximations. Correct answer.
  4. It provides approximate results faster than the speed layer.

Explanation

The batch layer processes the complete historical dataset to produce accurate, comprehensive views, while the speed layer handles recent data with low latency. The serving layer merges these to give both timely and corrected results.

Question 24Big Data Fundamentals and Alibaba Cloud Big Data Products

A streaming job must compute running totals per user over an unbounded event stream while tolerating out-of-order events. Which mechanism is most appropriate?

  1. Event-time windowing with watermarks Correct answer.
  2. Round-robin repartitioning of all events
  3. Full-table scans on each event
  4. Schema-on-write validation per record

Explanation

Event-time windowing groups events by when they occurred, and watermarks track how long to wait for late arrivals. Together they let the job produce correct running totals despite out-of-order data.

Question 25Big Data Fundamentals and Alibaba Cloud Big Data Products

An organization wants a unified platform where raw data in Object Storage Service can be queried with SQL and combined with structured warehouse tables. Which Alibaba Cloud capability best supports querying data across storage and warehouse layers?

  1. Querying OSS data directly through a data lake analytics or external table capability Correct answer.
  2. Relying on CDN caching to serve query results
  3. Using a message queue to join datasets
  4. Using only a relational database with manual exports

Explanation

Data lake analytics and external table features let engines query data held in object storage directly, avoiding full copies into the warehouse. This enables SQL joins that span raw files and structured warehouse tables.

Question 26Big Data Fundamentals and Alibaba Cloud Big Data Products

Which characteristic of big data refers to the speed at which data is generated and must be processed?

  1. Velocity Correct answer.
  2. Volume
  3. Veracity
  4. Variety

Explanation

The commonly cited dimensions of big data include volume (size), velocity (rate of generation and required processing speed), variety (range of formats), and veracity (trustworthiness). Velocity specifically concerns how quickly data arrives and how fast insight is needed.

Question 27Big Data Fundamentals and Alibaba Cloud Big Data Products

In the context of data warehousing, what does a data mart represent?

  1. A metadata catalog that records lineage for all warehouse tables
  2. A replication buffer that synchronizes the warehouse with source systems
  3. A subset of a data warehouse oriented to a specific department or business function Correct answer.
  4. A staging area that temporarily holds raw extracts before transformation

Explanation

A data mart is a smaller, focused slice of warehouse data scoped to a department, subject area, or use case. It may be dependent (sourced from the enterprise warehouse) or independent, but its defining trait is its narrower scope.

Question 28Big Data Fundamentals and Alibaba Cloud Big Data Products

Which Alibaba Cloud service is a fully managed data warehouse built on the MaxCompute engine that supports standard SQL analytics at scale?

  1. Log Service (SLS)
  2. Message Queue for Apache Kafka
  3. MaxCompute Correct answer.
  4. ApsaraDB for Redis

Explanation

MaxCompute is Alibaba Cloud's large-scale, fully managed data warehousing and computation service that runs SQL and other processing over massive datasets. The other options serve caching, log management, and messaging respectively.

Question 29Big Data Fundamentals and Alibaba Cloud Big Data Products

A team needs to run a distributed SQL query over a petabyte-scale table in MaxCompute and wants the engine to avoid moving all rows across the network. Which design choice most directly reduces shuffle volume?

  1. Increasing the number of small output files per partition
  2. Disabling compression on the table to speed decompression
  3. Partitioning and clustering the table on the columns used in joins and filters Correct answer.
  4. Storing all columns as variable-length strings

Explanation

Co-locating data by the join and filter keys through partitioning and clustering lets the engine prune irrelevant data and reduce cross-node data movement. Reducing shuffle is the main lever for large join performance.

Question 30Big Data Fundamentals and Alibaba Cloud Big Data Products

What is the primary purpose of a columnar storage format such as ORC or Parquet in an analytics workload?

  1. To enable index-based point lookups on single rows
  2. To read only the columns a query needs and compress similar values efficiently Correct answer.
  3. To guarantee row-level transactional locking for concurrent writers
  4. To enforce referential integrity between related tables

Explanation

Columnar formats store values of the same column together, so scans can skip unneeded columns and compression works well on homogeneous data. This suits analytic queries that aggregate a few columns over many rows.

Question 31Big Data Fundamentals and Alibaba Cloud Big Data Products

A data pipeline must handle records that arrive late and out of order while producing per-session aggregates. Which processing capability is most essential?

  1. Event-time windowing with allowed lateness and watermarks Correct answer.
  2. Converting every event to a fixed schema before ingestion
  3. Round-robin partitioning of records across workers
  4. Storing all events in a single unpartitioned file

Explanation

Event-time windowing with watermarks and allowed lateness lets the engine group records by when they occurred and wait a bounded time for stragglers. This directly addresses out-of-order and late-arriving events.

Question 32Big Data Fundamentals and Alibaba Cloud Big Data Products

In a distributed data warehouse, what does data partitioning primarily enable?

  1. Elimination of the need for data compression
  2. Automatic generation of surrogate keys
  3. Guaranteed strong consistency across all tables
  4. Pruning of irrelevant data so queries scan fewer records Correct answer.

Explanation

Partitioning splits a table into segments by a key such as date, so queries filtering on that key skip unrelated segments. This reduces scanned data and improves performance and cost.

Question 33Big Data Fundamentals and Alibaba Cloud Big Data Products

A company wants a serverless, interactive query service to analyze data sitting in Object Storage Service using standard SQL without provisioning a cluster. Which Alibaba Cloud product fits best?

  1. Data Lake Analytics Correct answer.
  2. Elastic Compute Service
  3. ApsaraDB for MongoDB
  4. Server Load Balancer

Explanation

Data Lake Analytics is a serverless, interactive analytics engine that queries data in object storage with SQL and integrates with other Alibaba Cloud data services. It removes the need to provision and manage a cluster.

Question 34Big Data Fundamentals and Alibaba Cloud Big Data Products

Which statement correctly distinguishes a data warehouse from a data lake in terms of schema handling?

  1. A warehouse typically applies schema-on-write, while a lake commonly uses schema-on-read Correct answer.
  2. Both enforce identical schema-on-write behavior at ingestion
  3. Neither supports schema definition of any kind
  4. A warehouse uses schema-on-read, while a lake always enforces schema-on-write

Explanation

Warehouses generally validate and structure data as it is loaded (schema-on-write), enabling optimized queries. Lakes typically store raw data and apply structure when it is queried (schema-on-read).

Question 35Big Data Fundamentals and Alibaba Cloud Big Data Products

A team must choose between a row-oriented OLTP database and a column-oriented analytical store for a workload dominated by wide-table scans with aggregations. Which choice and rationale is most appropriate?

  1. A column-oriented analytical store, because it excels at single-row transactional updates
  2. A row-oriented OLTP database, because it stores full rows contiguously for fast aggregation
  3. A row-oriented OLTP database, because it compresses homogeneous columns efficiently
  4. A column-oriented analytical store, because it reads only the referenced columns across many rows Correct answer.

Explanation

Analytic scans touch a few columns over many rows, so columnar layout reads less data and compresses well. Row stores are optimized for retrieving or updating whole rows in transactions.

Question 36Big Data Fundamentals and Alibaba Cloud Big Data Products

What is the main benefit of separating compute from storage in a modern cloud data architecture?

  1. It guarantees sub-millisecond query latency
  2. It removes the need for any data replication
  3. Each layer can scale independently and be sized for its own workload Correct answer.
  4. It eliminates the need for access control

Explanation

Decoupling lets storage grow independently of compute and lets compute clusters be created or released on demand. This improves elasticity and cost efficiency.

Question 37Big Data Fundamentals and Alibaba Cloud Big Data Products

A batch job joins a very large fact table with a small dimension table. Which technique most reduces network shuffle in a distributed engine?

  1. Broadcasting the small dimension table to every worker Correct answer.
  2. Sorting both tables on all columns before the join
  3. Repartitioning both tables by a random key
  4. Increasing the replication factor of the large table

Explanation

When one side of a join is small enough to fit in memory, broadcasting it to all workers lets each node join locally without shuffling the large table. This avoids the expensive repartitioning of the fact table.

Question 38Big Data Fundamentals and Alibaba Cloud Big Data Products

An analyst observes that a distributed aggregation job has one task running far longer than the rest while others finish early. Which root cause and remedy pairing is most plausible?

  1. Insufficient disk space on the coordinator; adding more network bandwidth
  2. Too many partitions evenly spread; merging partitions into one
  3. Excessive compression; disabling compression on all columns
  4. Skewed partition keys causing one task to receive most rows; salting or repartitioning the key Correct answer.

Explanation

A hot key concentrates a disproportionate share of rows into a single task, creating a long-tail straggler. Salting the key or repartitioning spreads that load across tasks.

Question 39Big Data Fundamentals and Alibaba Cloud Big Data Products

When designing a streaming pipeline that must guarantee exactly-once results into a sink, which combination is most appropriate?

  1. Checkpointed state with idempotent or transactional sink writes Correct answer.
  2. At-least-once delivery with no deduplication at the sink
  3. Random partitioning of events with no checkpointing
  4. Fire-and-forget writes with periodic full reloads

Explanation

Exactly-once end-to-end requires the engine to checkpoint its state and progress, combined with a sink that either deduplicates idempotently or commits transactionally. Without both, reprocessing after failure causes duplicates or loss.

Question 40Big Data Fundamentals and Alibaba Cloud Big Data Products

A data engineer needs to store many petabytes of raw clickstream logs at low cost and later run ad-hoc SQL over them on Alibaba Cloud. Which combination best matches the storage and query layers of such a data lake architecture?

  1. Load the raw logs into a relational OLTP database and query them with standard transactional SQL
  2. Store the raw logs in a message queue and query them directly with a stream processing operator
  3. Store the raw logs in Object Storage Service and query them with a serverless interactive SQL engine Correct answer.
  4. Keep the raw logs on local disks of each compute node and query them with a distributed file system

Explanation

A cloud data lake keeps raw, low-cost durable data in object storage and decouples it from elastic query engines that read it on demand. Object Storage Service provides the cheap, scalable persistence layer, while a serverless interactive SQL engine provides on-demand compute without provisioning clusters. This separation of storage and compute is the defining pattern for petabyte-scale ad-hoc analytics.

Question 41Data Integration and Development (DataWorks)

In DataWorks, which type of node is used to run a scheduled SQL statement that writes its result into a target table?

  1. Shell node
  2. ODPS SQL node Correct answer.
  3. Virtual node
  4. DataX node

Explanation

An ODPS SQL node runs SQL against a MaxCompute project and can be scheduled to insert or overwrite results in a target table. Shell and DataX nodes are for scripts and data synchronization, and a virtual node only controls scheduling dependencies.

Question 42Data Integration and Development (DataWorks)

A developer wants a DataWorks task to run only after another task has completed successfully. What should be configured?

  1. A higher priority value on the downstream node
  2. An alarm rule on the downstream node
  3. A scheduling dependency on the upstream node Correct answer.
  4. A longer timeout on the downstream node

Explanation

Scheduling dependencies define the order in which nodes run; the downstream node waits until the upstream node's instance succeeds. Priority, timeout, and alarms affect execution behavior but do not establish ordering.

Question 43Data Integration and Development (DataWorks)

Which DataWorks component provides a web-based SQL editor for interactively querying and developing against MaxCompute tables?

  1. DataStudio Correct answer.
  2. Operation Center
  3. Data Map
  4. Data Quality

Explanation

DataStudio is the development environment where users write and run SQL, author nodes, and manage workflows. Data Map is for metadata discovery, Data Quality for monitoring rules, and Operation Center for运维 of scheduled instances.

Question 44Data Integration and Development (DataWorks)

In DataWorks, what is the purpose of committing a node and then deploying it?

  1. To lock the node so no further edits are possible
  2. To move the node definition from the development environment into the production scheduling environment Correct answer.
  3. To convert the SQL node into a shell script
  4. To physically copy the source data into the production project

Explanation

Committing saves a version in development, and deploying publishes that version to the production environment where it is scheduled. Deployment does not move data or change the node type.

Question 45Data Integration and Development (DataWorks)

Which DataWorks feature is designed to move data between heterogeneous sources such as MySQL and MaxCompute on a schedule?

  1. Data Quality rule
  2. Data Integration (DataX-based sync task) Correct answer.
  3. Data Map lineage view
  4. Resource group autoscaling

Explanation

Data Integration provides reader/writer plugins built on DataX to synchronize data between different data stores. Data Quality, Data Map, and resource groups serve monitoring, metadata, and compute-scaling purposes respectively.

Question 46Data Integration and Development (DataWorks)

A recurring task must run once every day at 02:00. Which scheduling configuration expresses this?

  1. A minutely cycle with a 24-hour timeout
  2. A daily scheduling cycle with a scheduled time of 02:00 Correct answer.
  3. A weekly cycle with a dependency on itself
  4. An hourly cycle with priority set to 8

Explanation

The scheduling cycle sets the recurrence frequency and the scheduled time sets the clock time at which instances are generated. Timeout, priority, and self-dependency do not define daily recurrence.

Question 47Data Integration and Development (DataWorks)

In a DataWorks synchronization task, what does the 'dirty data' setting control?

  1. How many malformed records may be tolerated before the task fails Correct answer.
  2. Which resource group executes the task
  3. How often the task retries after a failure
  4. Which columns are encrypted during transfer

Explanation

The dirty data threshold (count or percentage) determines how many records that cannot be processed are allowed before the synchronization job is marked failed. Encryption, retry, and resource group are configured separately.

Question 48Data Integration and Development (DataWorks)

Which parameter placeholder is commonly used in DataWorks scheduling to reference the business date of the current instance?

  1. ${nodeid}
  2. ${projectid}
  3. ${random}
  4. ${bizdate} Correct answer.

Explanation

${bizdate} resolves to the business date associated with the scheduling instance, typically in yyyymmdd form. The other placeholders represent values such as a random string, node identifier, and project identifier.

Question 49Data Integration and Development (DataWorks)

A developer needs to reuse the same transformation logic across several workflows. Which DataWorks capability best supports this?

  1. Creating a reusable node template or node group referenced by multiple workflows Correct answer.
  2. Adding an alarm rule to each workflow
  3. Increasing the priority of each workflow
  4. Setting a longer timeout on each workflow

Explanation

Reusable templates or shared node groups let multiple workflows reference one definition, avoiding duplicated logic. Priority, alarms, and timeouts are runtime settings and do not promote reuse.

Question 50Data Integration and Development (DataWorks)

A DataWorks SQL node references an upstream table using ${bizdate} in its partition filter. What happens when the node runs for business date 20240115?

  1. The filter is removed and all partitions are read
  2. The filter resolves to the current system date at runtime
  3. The filter resolves to the previous month's first day
  4. The filter resolves to the partition for 20240115 Correct answer.

Explanation

Parameter substitution replaces ${bizdate} with the instance's business date, so the partition filter targets 20240115. It is not replaced by wall-clock time and is not stripped from the statement.