DA0-002 Data Acquisition and Preparation — Timed 10 Questions

Question 1

A data analyst needs to extract data from an API that returns JSON. The analyst wants to convert the JSON output into a tabular format for analysis. Which function in a scripting language is commonly used for this purpose?

10 minute time limit — choose an answer to begin.

Accepted Answer

json_normalize(). `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.

Answer

json.loads()

Answer

to_csv()

Answer

read_json()

Question 2

A data analyst sees this error in the ETL logs. What is the most likely cause?

Accepted Answer

The underlying data source was modified after the last refresh. The error indicates that the materialized view's underlying data has changed since its last refresh. Materialized views rely on change tracking (e.g., logs or timestamps) to perform incremental refreshes. If the source data was modified after the last refresh, the view cannot be incrementally refreshed—a full refresh is required instead. This mismatch between the view's snapshot and the source data is a common cause of refresh failures in ETL processes.

Answer

The source table was dropped

Answer

The analyst does not have permission to refresh the view

Answer

There is a network connection timeout

Question 3

A data engineer is configuring access to a data lake in Amazon S3. What does the JSON policy shown allow?

Accepted Answer

Read objects from the bucket. Based on the correct answer, the JSON policy shown implicitly grants the `s3:GetObject` action, which allows reading objects from the specified S3 bucket. In AWS IAM, reading objects requires explicit `GetObject` permission, making B the only correct choice.

Answer

Change user permissions

Answer

Delete objects from the bucket

Answer

Write objects to the bucket

Question 4

A healthcare organization is building a data warehouse to support population health analytics. The data sources include: (1) an electronic health record (EHR) system with a relational database containing patient demographics, diagnoses, and medications; (2) a claims system that generates CSV files daily; (3) patient-generated health data from mobile apps via a REST API returning JSON. The data engineer needs to design a data acquisition process that runs nightly. The EHR system has a change tracking mechanism that logs changes with timestamps. The claims CSV files are appended daily. The API supports filtering by date. The data warehouse uses a star schema with fact and dimension tables. The engineer must ensure data consistency and minimize load times. Which approach should the engineer take?

Accepted Answer

Extract only new and changed EHR data using change tracking, extract the full claims CSV (since it's append-only), and extract API data filtered by the last extraction date. It uses incremental extraction for the EHR system (via change tracking) and the API (via date filtering), while performing a full extraction of the claims CSV since it is append-only and small enough to reload nightly. This minimizes load times by avoiding full re-extraction of large, slowly changing datasets, and ensures data consistency by capturing only new or modified records. The star schema in the data warehouse is then populated efficiently from these targeted extracts.

Answer

Perform a full extraction of all data from all sources every night and load directly into the data warehouse

Answer

Use a staging area to land all raw data first, then transform and load

Answer

Extract the EHR data using change tracking, extract the full claims CSV, and extract the API data using a full dump

Question 5

A data analyst is merging two datasets from different departments. The analyst notices that the 'CustomerID' field in the first dataset is stored as an integer, while in the second dataset it is stored as a string with leading zeros. Which TWO steps should the analyst take to ensure successful data integration?

Accepted Answer

Use a left join and treat the CustomerID as a string after conversion.. Converting the integer CustomerID to a string ensures both datasets have a compatible data type for the join. This approach preserves the leading zeros in the second dataset, which are semantically significant (e.g., '00123' vs. 123). A left join is appropriate to retain all records from the primary dataset while matching on the converted key.

Answer

Perform the merge directly without transformation since databases handle type conversions automatically.

Answer

Strip all non-numeric characters from the string CustomerID before joining.

Answer

Convert the string CustomerID to an integer by removing leading zeros.

Question 6

Which THREE are common challenges when acquiring data from external APIs? (Choose three.)

Accepted Answer

Authentication requirements. Authentication requirements (A) are a common challenge because external APIs typically enforce OAuth 2.0 tokens, API keys, or signed requests, and credential handling, token refresh, and scope management must be implemented correctly before any data can be pulled. Rate limiting (C) is also a standard obstacle, since providers cap requests per second/minute/day (often returning HTTP 429 with Retry-After headers), forcing throttling, backoff, and batching strategies. Data volume constraints (E) are a real challenge too, as APIs impose pagination limits, maximum page sizes, result caps, and payload/timeout limits that require incremental extraction and pagination handling. Consistent data schemas (B) is not marked correct because schema variability is more typically associated with internal source systems or file-based feeds than with the defining acquisition challenges of external APIs. Guaranteed uptime (D) is not marked correct because no external API offers guaranteed uptime, and availability concerns, while real, are not one of the three challenges identified here.

Answer

Consistent data schemas

Answer

Guaranteed uptime

Question 7

Refer to the exhibit. An analyst runs this query before acquiring data from a PostgreSQL database. What is the primary purpose of this query?

Accepted Answer

To check for data freshness. The query shown in the exhibit retrieves the `last_analyzed` column from PostgreSQL's system catalog, which records when table statistics were last updated. Analyzing this timestamp helps the analyst determine if the table's data is fresh enough for the intended analysis, thus checking data freshness before acquisition.

Answer

To verify data types

Answer

To find primary keys

Answer

To identify duplicate tables

Question 8

Refer to the exhibit. A data engineer is setting up data acquisition from a cloud storage bucket with this policy. What does the policy enforce?

Accepted Answer

Access is restricted to a specific IP range. The bucket policy includes a condition using a source IP address with a CIDR block, which restricts access to requests originating from the specified IP range. This enforces network-level access control, allowing only clients within that IP range to perform the allowed actions (like GET requests) on the bucket. Option D correctly identifies this IP-based restriction.

Answer

Only objects with key 'data-bucket' can be accessed

Answer

Only GET requests are allowed

Answer

The storage container is publicly accessible

Question 9

Refer to the exhibit. An analyst sees this log during data acquisition. What action should be taken first?

Accepted Answer

Modify the ETL mapping for data types. The log shows a data type mismatch during ETL (Extract, Transform, Load) processing, where a date field is being read as a string. The correct first action is to modify the ETL mapping for data types to ensure the date string is properly cast or converted to the target date format, preventing data loss or corruption. This aligns with standard data acquisition best practices: adjust the transformation layer to handle source data anomalies before rejecting or altering the source.

Answer

Reject the entire dataset

Answer

Ignore warnings and continue

Answer

Correct the date string in the source

Question 10

During a data mining project, an analyst discovers that a significant number of records have a negative value for the age field. What is the most appropriate first step?

Accepted Answer

Investigate the source system for data entry errors.. The first step in handling anomalous data like negative ages is to investigate the source system for data entry errors. This aligns with the data mining process, where understanding the root cause of data quality issues is critical before applying any imputation or removal techniques. Without investigation, you risk masking systemic problems that could affect all records.

Answer

Impute using regression.

Answer

Replace negative age with the mean age.

Answer

Remove all records with negative age.